EPA 600/R-07/046 | June 2007 | www.epa.gov/ord 




An Approach to Developing 
Nutrient Criteria for 

t 

Pacific Northwest Estuaries: 

A CASE STUDY OF YAOUINA ESTUARY. OREGON 


Office of Research and Development 

National Health and Environmental Effects Research Laboratory, Western Ecology Division 





V '— ■ 



LC Cor >tro/ Number 



2008 


614454 



EPA 600/R-07/046 


An Approach to Developing Nutrient Criteria for Pacific Northwest Estuaries: 

// 

A Case Study of Yaquina Estuary, Oregon 



Principal Authors 

Cheryl A. Brown, Walter G. Nelson, Bruce L. Boese, Theodore H. DeWitt, 

Peter M. Eldridge, James E. Kaldy, Henry Lee II, James H. Power, and David R. Young 

US EPA Office of Research and Development 
National Health and Environmental Effects Research Laboratory 

Western Ecology Division 

June 2007 


Additional Contributors 

Robert J. Ozretich, Anne C. Sigleo, Christina Folger, Katharine M. Marko, 
Patrick J. Clinton, Faith A. Cole, TChris Mochon-Collura, David T. Specht, 

and Anthony D’Andrea 



Preface 


Disclaimer 

The information in this document has been funded wholly by the U.S. Environmental Protection 
Agency. It has been subjected to review by the National Health and Environmental Effects 
Research Laboratory and approved for publication. Approval does not signify that the contents 
reflect the views of the agency, nor does mention of trade names or commercial products 
constitute endorsement or recommendation for use. 

Citation 

Brown, C.A., W.G. Nelson, B.L. Boese, T.H. DeWitt, P.M. Eldridge, J.E. Kaldy, H. Lee II, J.H. 
Power, and D.R. Young. 2007. An Approach to Developing Nutrient Criteria for Pacific 
Northwest Estuaries: A Case Study of Yaquina Estuary, Oregon. USEPA Office of 
Research and Development, National Health and Environmental Effects Laboratory, 
Western Ecology Division. EPA/600/R-07/046. 

Acknowledgements 

We greatly appreciate the assistance of numerous employees of Dynamac Inc. who 
provided technical support to Western Ecology Division (WED) for many aspects of the work 
described in this document, including Adam DeMarzo, Lucas Nipp, Stacy Strickland, and Una 
Monaghan. We appreciate the administrative support provided by Janet Lamberson (WED). 
Gary Arnold with the Oregon Department of Environmental Quality (ODEQ) provided helpful 
review comments on this document as well as information on water quality standards and 
historical information. Tim McFetridge (ODEQ) and Lee Ritzman (Public Works Department, 
City of Newport) provided information on point source inputs. Deneb Karentz (University of 
San Francisco) kindly provided some unpublished historical data. Anne Fairbrother, Marilyn ten 
Brink, Robert Ozretich, and James Hagy provided comments on the manuscript which improved 
the quality of the report. Numerous WED staff contributed unpublished data to this report, 
including Bruce Boese (Z. marina lower margin and macroalgae), Cheryl Brown (water quality), 
Pat Clinton (maps of Z. marina and macroalgae coverage), Ted DeWitt (macroinvertebrate and 
benthic flux), Peter Eldridge (water quality), James Kaldy (Z marina and stable isotope), Henry 
Lee (watershed landscape), Walt Nelson (epiphyte data), Robert Ozretich (cruise and irradiance), 
David Specht (datasonde), and David Young (macroalgae and Z. marina data). 

ii 


Executive Summary 


A proposed approach that could be used by the State to develop nutrient criteria for the 
Yaquina Estuary, Oregon is presented. The approach is based on a synthesis of research results 
derived from field sampling at multiple temporal and spatial scales, assembling data to construct 
historical trends in water quality parameters, and a variety of modeling approaches. 

Yaquina Estuary is a small, drowned, river valley estuary located along the central 
Oregon coast. Approximately 48% of the estuarine area is intertidal. The designated uses within 
the Yaquina Estuary and River include aquatic life harvesting (shellfish growing and fishing), 
agricultural (livestock watering), municipal (public water supply), recreation (water contact 
recreation), ecological (resident fish and aquatic life, salmonid spawning and rearing, 
anadromous fish passage) and aesthetics. 

Spatial and temporal variability in water quality indicators were assessed for multiple 
water quality parameters, including nitrogen, phosphorus, chlorophyll a , dissolved oxygen, total 
suspended solids, and water column light attenuation. Spatial scales examined included variation 
within the Yaquina Estuary, as well as comparison of some parameters to short term studies of 
six additional Oregon estuaries, and comparison to a single sampling of 14 additional Oregon 
estuaries conducted by the US EPA National Coastal Assessment program. Green macroalgal 
occurrence was evaluated to determine whether this was an appropriate indicator for nutrient 
responses within the Yaquina Estuary. Lower depth limits for the seagrass (Zostera marina ) 
were determined in order to estimate the minimum light requirements for sustaining seagrass. 
Field results were used to confirm output from a Seagrass Stressor-Response Model. 

Because there were limited data for applying the reference condition approach for the 
class of estuaries similar to the Yaquina Estuary, we used in situ observations within Yaquina 
Estuary as a basis for determining an Estuarine Reference Condition. Cumulative distribution 
functions (CDFs) were produced for water quality variables for the Yaquina Estuary and 
compared to CDFs for other Oregon estuaries using two independent data sets. Key percentiles 
(25 th , 50 th , 75 th ) for water quality parameters were used as inputs to a Seagrass Stressor-Response 
Model to determine whether particular percentile values would be adequately protective of 
seagrass within the Yaquina Estuary. 


in 


The Yaquina Estuary has strong seasonal variation in the magnitude of nutrient loading 
and in the dominant nutrient sources. Response variables (particularly, chlorophyll a and 
dissolved oxygen) exhibited similar patterns of seasonal variation. During the wet season 
(November-April), riverine nitrogen inputs dominate, whereas during the dry season (May - 
October) oceanic nitrogen sources dominate. Riverine inputs are primarily related to the 
presence of nitrogen-fixing red alder (Alnus rubra) trees in the watershed. There are also strong 
zonal differences in nutrient levels, response variables, and dominant nutrient sources within the 
Yaquina Estuary. In the lower estuary (Zone 1), water quality conditions are strongly influenced 
by ocean conditions, while in the upper portions of the estuary (Zone 2), watershed and point 
source inputs increase in importance. 

We suggest that criteria be developed for wet and dry seasons to address the strong 
seasonal variation in nutrient loads and sources. Dry season criteria (May-October) are most 
important, since during the wet season, there appears to be little utilization of nutrients within the 
estuary, and chlorophyll a levels are low and the dissolved oxygen concentrations are high. 

Thus, it is not clear that wet season criteria are needed within the Yaquina Estuary. We suggest 
that separate criteria be developed for Zones 1 and 2, with dry season criteria for Zone 2 a first 
priority. The high degree of ocean-estuary coupling found for Zone 1 within the Yaquina 
Estuary with associated short-term variability in water quality parameters suggests that 
monitoring for compliance with nutrient criteria in this region may be problematic. During the 
dry season, phosphate, nitrate, chlorophyll a , and dissolved oxygen levels in Zone 1 are primarily 
determined by ocean conditions and separation of oceanic from anthropogenic inputs would 
require, at the least, continuous monitoring capability, and may require additional techniques. 

Use of Total Suspended Solids (TSS) as a water quality criterion may not be practical due 
to inconsistent spatial and temporal patterns relative to that of adjacent Oregon estuaries. 
Macroalgal biomass response within Yaquina Estuary appears to be primarily driven by oceanic 
nitrogen input, and thus does not appear to be useful as an indicator of cultural eutrophication. 

Based on weight of scientific evidence, we conclude that Yaquina Estuary is not 
exhibiting symptoms of cultural eutrophication. Thus, following the recommendations in U.S. 
EPA (2001), median values could be used as criteria for most water quality parameters. The 
Seagrass Stress-Response Model confirmed that the median percentile for water clarity (kj) 
would be protective of the existing eelgrass (.Zoster a marina) habitat in the Yaquina Estuary. 


IV 


Modeling results were consistent with analysis of seagrass depth limits which indicated that the 
median kj provided for persistence of seagrass at depths within the two estuarine zones that were 
comparable to current depth distributions. 

The current Oregon DO criterion of 6.5 mg l' 1 should be adequately protective of 
estuarine resources, but is closer to the 25 th percentile value rather than the median value for DO 
data in Zone 2. Recent DO measurements demonstrate that hypoxic water is imported into the 
estuary from the coastal shelf during the dry season. As a result, exceedances of the DO 
criterion should be expected particularly in Zone 1. The current Oregon chlorophyll a criterion 
of 15 pg l' 1 is approximately 3 times greater than the median value for Zone 2. The chlorophyll a 
criterion is determined as a 3-month average, and if chlorophyll a levels were to approach the 
present criterion for such a time period, significant trophic shifts in the estuary would be likely. 
Thus, the current chlorophyll a criterion may not prevent some impacts on designated use. 


Potential dry season criteria for the Yaquina Estuary based on median values for all parameters 
except for DO. 

Parameter (units) 

Zone 1 

Zone 2 

DIN (nM) 

14 

14 

Phosphate (pM) 

1.3 

0.6 

Chlorophyll a (pg f 1 ) 

3 

5 

Water Clarity (m 1 ) 

0.8 

1.5 

Dissolved Oxygen (mg f 1 ) 

6.5 


v 











Table of Contents 


Preface.ii 

Executive Summary.iii 

List of Figures.ix 

List of Tables.xii 

1. Introduction.1 

1.1 Purpose of This Case Study.1 

1.2 Nutrient Criteria Objective.1 

1.3 Designated Uses of Yaquina River and Estuary, Impairments and Assessments.2 

1.4 Oregon Estuarine Water Quality Criteria.4 

1.5 Summary of Yaquina Case Study Approach.4 

2. Description of Study Area.6 

2.1 Physical Characteristics of the Estuary.6 

2.2 Biotic Characteristics.9 

2.3 Land Use and History of Anthropogenic Modifications.13 

2.4 Classification of the Yaquina Estuary.17 

2.5 Conceptual Model for Yaquina Estuary.18 

3. Description of Sources/Sinks of Nutrients.20 

3.1 Background.20 

3.2 Nitrogen Loading to Yaquina Estuary.20 

*3.2.1 Watershed.21 

3.2.2 Ocean Input.22 

3.2.3 Benthic Processes.23 

3.3 Zonation Based Upon Nitrogen Sources.24 

4. Data Sources and Methods.26 

4.1 Yaquina Estuary Data.26 

4.1.1 Recent Data.26 

4.1.2 Additional Data Sources.27 

4.2 Oregon Estuarine Classification Study.30 

4.3 National Coastal Assessment (NCA).30 

4.4 Percentile Approach. 31 

vi 
































4.5 Statistical Analysis.32 

5. Spatial and temporal patterns in water quality parameters in the Yaquina Estuary.34 

5.1 Salinity.34 

5.2 Nutrients.35 

5.3 Chlorophyll a .37 

5.4 Nutrient Limitation and Primary Productivity.38 

5.5 Relationships between TN and TP and Chlorophyll a .40 

6. Nitrogen and Phosphorus as Water Quality Criteria.43 

6.1 Seasonal, Zonal, and Long-term Trends in N and P.43 

6.2 Percentile Approach for Nitrogen and Phosphorous.48 

7. Chlorophyll a as a Water Quality Response Measure.50 

7.1 Seasonal, Zonal, and Long-Term Trends in Chlorophyll a .50 

7.2 Percentile Approach for Chlorophyll a .52 

8. Dissolved Oxygen as a Water Quality Response Measure.53 

8.1 Seasonal, Zonal and Long-term Trends in Dissolved Oxygen.53 

8.2 Percentile Approach for Dissolved Oxygen.58 

9. Water Clarity (kj) and Turbidity as Water Quality Response Measures.60 

9.1 Seasonal and Zonal Patterns in Water Clarity and Turbidity.60 

9.2 Percentile Approach for Water Clarity and TSS.62 

10. Macroalgal Biomass as a Water Quality Response Measure.65 

10.1 Introduction.65 

10.2 Approach.65 

10.3 Results and Discussion.65 

10.3.1 Annual Variation: 1997 - 1998.65 

10.3.2 Seasonal Variation: 1999-2000.66 

10.4 Percentile Approach.68 

10.5 Comparisons with Findings from Other Regions.69 


11. Submerged Aquatic Vegetation (SAV) as a Management Objective (Designated Use) ...71 

11.1 Background.71 

11.2 Spatial Seagrass Patterns.73 

11.3 Temporal Seagrass Patterns.75 


Vll 
































11.4 Water Clarity and Seagrass Lower Depth Limit.77 

11.4.1 Background.77 

11.4.2 Methods.78 

11.4.3 Relationship between Lower Margin and Water Clarity.78 

11.5 Epiphyte Patterns and Impact on Z. marina .82 

11.5.1 Spatial and Temporal Patterns in Epiphytes.82 

11.6 Zostera marina Light Requirements.84 

12. Stress-Response Approach for Protection of SAV.88 

12.1 Introduction.88 

12.2 Description of Model.88 

12.3 Model Simulations and Input Data.90 

12.4 Results.91 

12.5 Discussion.94 

13. Conclusions and Recommendations.97 

13.1 Recommendations.99 

References.102 

Appendix A: Benthic Processes in Yaquina Estuary.122 

Appendix B: Description of Methods and Quality Assurance Procedures.128 

Appendix C: Classification of Oregon Estuaries.156 

Appendix D: Survey of Effects of Macroalgae on Biota.159 

Appendix E: Stress-Response Model Calibration, Input Data, and Results.162 


vm 























List of Figures 

Figure 2.1. Location map of Yaquina Estuary. 6 

Figure 2.2 Map of watershed of the Yaquina Estuary, showing the two primary tributaries.7 

Figure 2.3 Monthly discharge statistics (Yaquina River + Big Elk Creek). 8 

Figure 2.4 a) False-color, infrared aerial photography mosaic of Yaquina Estuary and 

b) map of intertidal seagrass and macroalgae in Yaquina Estuary.12 

Figure 2.5 Populations in the cities of Toledo and Newport, Oregon from 1890 to 2005. 14 

Figure 2.6 Number of logs floated or rafted on the Yaquina River from 1918 to 1978. 16 

Figure 2.7 Conceptual model of factors influencing nutrient and response variables in the 

Yaquina Estuary.19 

Figure 3.1 Modeled contribution of WWTF effluent, riverine, and oceanic sources to DIN at 

5 locations in the estuary during January - September of 2004.25 

Figure 5.1 Salinity versus distance from mouth of the estuary during the a) dry and 

b) wet seasons.34 

Figure 5.2 Spatial variation in dry season NO 3 + NO 2 .36 

3 - 

Figure 5.3 Spatial variation in dry season PO 4 .36 

Figure 5.4 Dry season chlorophyll a versus salinity (all stations from 1974-2006).38 

Figure 5.5 Mean dry season (2006) total nitrogen (TN) and total phosphorous (TP) versus 

distance from mouth of estuary.41 

Figure 5.6 Mean dry season (2006) chlorophyll a versus distance from mouth of estuary.41 

Figure 5.7 Total nitrogen (TN) versus chlorophyll a for the dry season (2006) with data 

divided by zones.42 

Figure 5.8 Total phosphorous (TP) versus chlorophyll a during the dry season (2006) with 

data divided by zones.42 

Figure 6.1 Comparison of historic and recent NO 3 +N0 2 during the dry season in a) Zone 1 

and b) Zone 2.46 

Figure 6.2 Comparison of historic and recent N0 3 +N0 2 during the wet season in a) Zone 1 

and b) Zone 2.46 


IX 




















Figure 6.3 Comparison of historic and recent PO 4 during the dry season in a) Zone 1 and 

b) Zone 2.47 

3 - 

Figure 6.4 Comparison of historic and recent PO 4 during the wet season in a) Zone 1 and 

b) Zone 2.47 

Figure 7.1. Box plot of monthly chlorophyll a data from the Yaquina Estuary (all stations from 

1973-2006). 51 

Figure 7.2 Comparison of historic and recent dry season chlorophyll a for a) Zone 1 and 

b) Zone 2 in the Yaquina Estuary.51 

Figure 8.1 Seasonal pattern in dissolved oxygen at all locations and all years in the Yaquina 
Estuary and River with squares and triangles representing samples from Zones 1 and 2, 

respectively.54 

Figure 8.2 Interannual trend in residual dissolved oxygen values during 1960 to 1986 for a) 

Zone 1 and b) Zone 2.55 

Figure 8.3 a) Time-series of dissolved oxygen and salinity and b) salinity versus dissolved 
oxygen showing import of hypoxic ocean water at a station 3.7 km from mouth of 

estuary.57 

Figure 9.1. Spatial variation in turbidity during wet and dry seasons (1998-2006).60 

Figure 9.2 Light attenuation coefficients (kj) versus distance from the mouth of the estuary from 

cruise data (years 1998 to 2006), with filled and open symbols representing dry and wet 

seasons, respectively.*..61 

Figure 9.3. Median monthly light attenuation coefficients from the continuous data set at 

5 locations in Yaquina Estuary (1999-2003).62 

Figure 10.1 Photomap of intertidal vegetation in Yaquina Estuary from aerial surveys of 

July 23, 1997 and August 10, 1998.67 

Figure 10.2 Average percent cover and biomass values (+ 1 std. err.) of benthic green 

macroalgae in the Yaquina Estuary between June 1999 and May 2000. 68 

Figure 11.1 Spatial distribution of Yaquina Estuary eelgrass.72 

Figure 11.2 Z. marina depth distribution in the marine dominated portion (Zone 1) of 

Yaquina Estuary. 74 


x 
















Figure 11.3 Z. marina depth distribution in the river dominated portion (Zone 2) of 

Yaquina Estuary.74 

Figure 11.4 Comparison of the spatial distribution of Z. marina in a portion of the 

Yaquina Estuary (see inset on Figure 11.1) from 1997, 2000, and 2004.76 

Figure 11.5 Historical distribution (mid 1970’s) of Z. marina from the Oregon Estuary Plan 

Book (Cortright et al., 1987). 77 

Figure 11.6 Relationship between distance from mouth of the estuary and the lower depth 

limit (below Mean Sea Level) for Z. marina .79 


Figure 11.7 Relationship between Z. marina maximum depth limit (m below MSL) and kj .... 81 
Figure 11.8 Temporal relationship of epiphytic biomass per unit leaf area on Z. marina external 

leaves in the Yaquina Estuary, 2000-2003. 83 

Figure 11.9 Epiphyte biomass per unit leaf area on old (external) and young (internal) Z. marina 

leaves by season (wet or dry) and salinity zone in the Yaquina Estuary.83 

Figure 11.10 Linear regression relationship between the percent of light reduction to log(x+l) 

transformed epiphyte biomass per unit Z. marina surface area.84 

Figure 12.1 Observed eelgrass distribution (yellow cross-hatch) and simulated eelgrass (green) 

depth distribution based on the median case.92 

Figure 12.2 Observed eelgrass distribution (yellow cross-hatch) and simulated eelgrass (green) 

depth distribution based on the 25 th percentile case.93 

Figure 12.3 Observed eelgrass distribution (yellow cross-hatch) and simulated eelgrass (green) 
depth distribution based on the 75 th percentile case.94 


xi 












List of Tables 


Table 1.1 Designated uses and water quality attainments for Yaquina Estuary/River.3 

Table 1.2 Selected water quality criteria for Oregon estuaries.4 

Table 3.1 Comparison of the magnitude of nitrogen sources during wet and dry seasons for 

Yaquina Estuary, Oregon.23 

Table 4.1 Sampling frequency for cruise data collected by U.S. EPA from 1998-2006.28 

Table 4.2 Summary of historic data compiled include source of the data, temporal and spatial 

sampling frequency of the data set and parameters measured.29 

Table 6.1 Comparison of historic and recent NO 3 +NO 2 (pM) concentrations in the Yaquina 

Estuary.45 

Table 6.2 Comparison of historic and recent PO 4 (pM) concentrations in the Yaquina 

Estuary.45 

Table 6.3 Percentiles for DIN (pM) calculated using Yaquina (1998-2006), Classification (2004- 
2005), and NCA Oregon estuaries (1999-2000) data sets.49 

Table 6.4 Percentiles for Po/ (pM) using Yaquina (1998-2006), Classification (2004-2005), 

and NCA Oregon estuaries (1999-2000) data sets. NCA and Classification values are for 

dry season only, while Yaquina include dry and wet season values.49 

Table 7.1 Percentiles for chlorophyll a (pg l' 1 ) calculated using Yaquina (1998-2006), 

Classification (2004-2005), and NCA Oregon estuaries (1999-2000) data sets.52 

Table 8.1 Percentiles for dissolved oxygen (mg T ) calculated Yaquina (1998-2006), 

Classification (2004-2005), and NCA Oregon Estuaries (1999-2000) data sets.59 

Table 9.1 Percentiles for light attenuation coefficient kj (nf') calculated using continuous (1999- 
2003) and cruise (1998-2006) data sets from the Yaquina Estuary for dry and wet 

seasons.64 

Table 9.2 Percentiles for TSS (mg l' 1 ) calculated using Yaquina (1998-2004), Classification 

(2004-2005), and NCA (1999-2000) data sets.64 

Table 10.1 Percentiles for benthic green macroalgae biomass (gdw m ) for Yaquina Estuary 

(1998 - 2004, Zone 1 only) and the Classification data set (2004 - 2005).69 

Table 11.1 Comparison of mean and range of percent of water column surface irradiance needed 
to maintain Z. marina at its colonization depth from published data and from Yaquina 
Estuary data.81 

xii 

















Table 12.1 Input data from percentile approach for different SRM simulations.90 

Table 12.2 Summary of the protective capacity of different potential criteria derived from 

Yaquina Estuary percentile data.92 

Table 12.3 Dry season median light attenuation and DIN values for Yaquina Estuary as 

compared to water quality management targets for other estuaries that are protective of 

eelgrass habitat.96 

Table 13.1 Potential dry season criteria for the Yaquina Estuary based on median values for all 
parameters except for DO.101 


xiii 


















































































































































1. Introduction 


1.1 Purpose of This Case Study 

The Office of Science and Technology (OST), Office of Water, U.S. EPA provides 
guidance to the States and tribes for developing nutrient criteria for estuarine and coastal waters. 
The Office of Research and Development, National Health and Environmental Effects 
Laboratory (NHEERL) has been conducting research to support improvements to the scientific 
basis for estuarine nutrient criteria for over 5 years under the NHEERL Aquatic Stressors 
Research Program. Parallel research efforts have been on going at the Western (WED), Gulf 
(GED) and Atlantic Ecology Divisions (AED). To support the OST criteria effort, NHEERL 
scientists have synthesized the research results of field sampling, trend analyses, and modeling 
approaches to produce nutrient criteria case studies for Yaquina Estuary, OR and Pensacola Bay, 
FL. Each case study describes one or more approaches that may be used for establishing nutrient 
criteria and offers specific recommendations for the particular system. Here we describe a 
recommended approach for developing nutrient criteria values for the Yaquina Estuary. 

1.2 Nutrient Criteria Objective 

The Nutrient Criteria Technical Guidance Manual: Estuarine and Coastal Marine Waters 
(U.S. EPA, 2001) provides a detailed summary description of the nutrient criteria development 
process in Section 1.4 of the manual. This guidance defines two objectives for establishment of 
numeric nutrient criteria: 

To reduce the anthropogenic component of nutrient overenrichment to levels that restore 
beneficial uses (i.e. described as designated uses by the CWA), or to prevent nutrient 
pollution in the first place. 

Quantitative, long term data on the status of eutrophication in most Oregon estuarine systems is 
limited (Bricker et al., 1999). The EPA National Coastal Assessment (U.S. EPA, 2004a) 
sampled Oregon estuaries for a variety of water quality indicators in 1999-2000, and concluded 
that there was little evidence of eutrophication effects in Oregon estuaries. Additional qualitative 
and quantitative assessments of Oregon estuaries by WED generally support the conclusions of 
the NCA report, but also suggest that in limited regions under certain circumstances, water 
quality problems may arise. Thus, the principle objective in developing nutrient criteria for the 


1 


Yaquina Estuary is to prevent future degradation of estuarine water quality and accompanying 
loss of beneficial uses from the system. 

1.3 Designated Uses of Yaquina River and Estuary, Impairments and Assessments 

The Yaquina River and Estuary have many designated uses, including aquatic life 
harvesting (shellfish growing and fishing), agricultural (livestock watering), municipal (public 
water supply), recreation (water contact recreation), ecological (resident fish and aquatic life, 
salmonid spawning and rearing, anadromous fish passage) and aesthetics (Table 1.1). Causes for 
impairment listings in the Yaquina Estuary and River include pathogens, thermal modifications, 
diminished biologic integrity, and organic enrichment/low dissolved oxygen (Table 1.1). Most 
of the impairments occur in the Yaquina River, with the exception of fecal coliform impairment 
which occurs in the lower portion of the estuary. 

The Oregon Department of Environmental Quality (ODEQ) assessed the water quality in 
the Oregon Mid Coast Basin, which includes the Yaquina River during 1986-1995 (Cude, 1995). 
The following is an excerpt from this assessment. 

Nitrate nitrogen is the primary limiting factor on water quality throughout the Mid Coast 
basin. High levels of nitrates accompanied by increases in total phosphates, total solids, 
and biochemical oxygen demand, appear during periods of heavy precipitation. 
Nutrient-rich erosion products deposited during storm events place a high demand on 
available dissolved oxygen in the water. These products may be naturally occurring, but 
are more likely the result of non-point source pollution. 

As part of this ODEQ study, an Oregon Water Quality Index (OWQI) that incorporates 
temperature, dissolved oxygen, biochemical oxygen demand, pH, total solids, ammonia and 
nitrate nitrogen, total phosphorous and fecal coliforms was developed. Based on this index, 
water quality for the Yaquina River (at Rivermile 24.9) was categorized as poor during the fall, 
winter, and spring, and good during the summer. The water quality in the Yaquina estuary was 
reassessed in 2006 using data from water years 1996-2005 (Mrazik, 2006). In this more recent 
assessment, the Yaquina River was assessed as having good condition throughout the year. 

In the National Estuarine Eutrophication Assessment (Bricker et al., 1999), the Yaquina 
Estuary was placed in the low category of eutrophication status based on a qualitative assessment 
that it exhibited few symptoms of eutrophication. However, conditions were expected to worsen 


2 


by 2020, primarily as a result of increasing population pressures. The confidence levels for the 
assessment of the eutrophic conditions in the Oregon region were low due to paucity of data 
(Bricker et ah, 1999). 


Table 1.1 Designated uses and water quality attainments for Yaquina Estuary/River. (Source: 
U.S. EPA National Assessment Database, 305(b) Lists/Assessment Unit Information Year 
2002; http://www.epa.gov/waters/305b/index.html) 


Rivermile 

State Designated 

Use 

Attainment 

Status 

Threatened 

Basis of Impairment 
Classification 

0-6.3 

Shellfish Growing 

Not Supporting 

No 

Fecal Coliform 

5.1-15.4 

Shellfish Growing 

Not Supporting 

No 

Fecal Coliform 

6.3-14.2 

Shellfish Growing 

Fully Supporting 

No 

NA 

15.4-27.6 

Resident Fish and 
Aquatic Life 

Fully Supporting 

No 

NA 

Salmonid Fish 
Spawning 

Water Contact 
Recreation 

Salmonid Fish 
Rearing 

Not Supporting 

No 

Thermal Modifications 

Anadromous Fish 
Passage 

27.6-42 

Resident Fish and 
Aquatic Life 

Partial 

Supporting 

No 

Biologic Integrity 1 

27.6-57.5 

Aesthetics 

Fully Supporting 

No 

NA 

Fishing 

Livestock Watering 

Resident Fish and 
Aquatic Life 

Water Contact 
Recreation 

Water Supply 

Salmonid Fish 
Rearing 

Not Supporting 

No 

Organic 

Enrichment/Low 
Dissolved Oxygen 

Salmonid Fish 
Spawning 

Anadromous Fish 
Passage 


'Aquatic communities (primarily macroinvertebrates) which are <60% of the expected 
reference community for multimetric and multivariate model scores are considered impaired. 


3 





























1.4 Oregon Estuarine Water Quality Criteria 

Water quality criteria standards are developed to protect beneficial uses. The State of 
Oregon presently has numeric water quality criteria for chlorophyll a and dissolved oxygen for 
estuarine waters (Table 1.2). The dissolved oxygen (DO) criterion for estuarine waters is 
primarily based on the freshwater literature and on salmon and trout requirements (ODEQ, 

1995). This DO criterion is relatively high compared to DO criterion for other estuaries, such as 
the Chesapeake Bay (U.S. EPA, 2003). In addition to the criteria in Table 1.2, the state wide 
narrative criteria states that: 

“where a less stringent natural condition of a water of the State exceeds the numeric 
criteria ”... “the natural condition supersedes the numeric criteria and becomes the 
standard for that water body. ” 

In the narrative criteria, the natural condition refers to non-anthropogenic conditions. A review 
of the DO criterion (ODEQ, 1995) found that the 6.5 mg T 1 may be difficult to achieve in Oregon 
estuaries during the summer due to natural background conditions. If it is not achievable due to 
natural background conditions, then the background conditions become the criteria. 


Table 1.2 Selected water quality criteria for Oregon estuaries. 

Parameter 

Estuarine Criterion 

Water Quality Limited Determination 

Chlorophyll a 

15 Mg r 1 

Average based on minimum of 3 samples collected 
over any 3 consecutive months at a minimum of one 
representative location exceeds criterion 1 

Dissolved Oxygen 

6.5 mg l 1 

Greater than 10% of samples exceed the criterion and 
a minimum of at least 2 exceedances of the criterion 
for the time period of interest. A minimum of 5 
representative data points per site collected on 
separate days per applicable time period. Daily 
means of continuous data represents 1 data point. 2 

Note: Criterion applies to river and estuaries; ^Estuarine waters defined as those with 

conductivity > 200 pS cm' 1 for dissolved oxygen criterion. Other dissolved oxygen 
criterion applies to freshwater region. 


1.5 Summary of Yaquina Case Study Approach 

In Chapter 2, we provide a description of the watershed and estuary, including a 
description of landuse in the watershed, and a brief history of anthropogenic activities in the 
estuary and watershed. Chapter 3 presents a summary of the nitrogen inputs to Yaquina Estuary 
with discussion of the seasonality and magnitude of natural and anthropogenic sources. A 


4 









summary of data used in this report and analysis techniques are presented in Chapter 4. 

Chapter 5 provides information on spatial and temporal patterns in water quality data, including 
important factors influencing water quality distributions. Seasonal, zonal, and long-term trends 
in causal (nitrogen and phosphorous) and response (chlorophyll a, dissolved oxygen, and water 
clarity) variables are presented in Chapters 6-9. These chapters also include cumulative 
distribution functions for causal and response variables and comparison of water quality 
conditions in Yaquina Estuary to those in other Oregon estuaries. In addition, comparisons of 
observations from Yaquina Estuary to existing State of Oregon chlorophyll a and dissolved 
oxygen criteria are summarized in Chapters 7 and 8. Chapter 10 examines the usefulness of 
macroalgal biomass as a response variable for the Yaquina Estuary, including descriptions of 
seasonal, interannual, and zonal patterns in macroalgal biomass and factors which influence its 
distribution. Chapter 11 provides a description of the distribution, variability, and factors 
influencing Zostera marina habitat in the estuary as well as light requirements for this species. 
Chapter 12 provides a demonstration of using a mechanistic stress-response model for Z. marina 
to assess whether specific water clarity percentiles are protective of existing habitat. A summary 
of the results of this study and recommendations are provided in Chapter 13. 


5 




2. Description of Study Area 


2.1 Physical Characteristics of the Estuary 

Yaquina Estuary is a small, drowned, river valley estuary located along the central 
Oregon coast (latitude = 44.62°N, longitude = 124.02° W) of the United States (Figure 2.1) with 
an estuarine surface area of 19 km and a watershed area of 650 km (Figure 2.2; Lee et al., 
2006). Approximately 48% of the estuarine area is intertidal. This estuary experiences mixed 
semidiurnal tides and is mesotidal with a mean tidal range of approximately 1.9 m and a tidal 
prism volume of 2.4 x 10 m (Shirzad et al., 1988). Yaquina Estuary has jetties that extend into 
the Pacific to the 10-m depth contour. Due to the small volume of the estuary (25 x 10 m at 
Mean Lower Low Water (MLLW)) and the strong tidal forcing, there is close coupling between 
the estuary and the coastal ocean. Approximately 70% of the volume of the estuary is exchanged 


with the coastal ocean during each tidal cycle (Karentz and Mclntire, 1977). 



(Zone 1) and “riverine dominated” (Zone 2) segments (Lee et al., 2006) based on the 
relative proportion of oceanic-derived nutrients versus terrestrially-derived nutrients. 


6 









Yaquina Estuary receives freshwater inflow primarily from two tributaries, the Yaquina 
River and Big Elk Creek, which have similarly sized drainage areas and contribute 
approximately equally to freshwater inflow (Figure 2.2; State Water Resources Board, 1965). 
The long-term median freshwater input to Yaquina Estuary is 7.5 m s' . There is a strong 
seasonal pattern in freshwater input to the Yaquina Estuary (Figure 2.3). During the months of 
November through April, the Oregon coast receives high precipitation and the estuary is river 
dominated. Beginning in May and continuing through October, there is a decline in the riverine 
freshwater inflow and the estuary switches from riverine to marine dominance. For this 
document, we defined the wet season (November - April) as months when the median monthly 
discharge exceeds the long-term median annual discharge and the dry season (May- October) as 
months when the median monthly discharge is less than the long-term median. The estuary is 
well mixed under low flow conditions, and partially- to well- mixed during winter high inflow 
conditions (Burt and McAlister, 1959; Kulm and Byrne, 1966). The flushing time of the estuary 
during the dry season varies from 1 day near the mouth to 9 days in the upstream portions (Choi, 
1975). 



Figure 2.2 Map of watershed of the Yaquina Estuary, showing the two primary tributaries 
(Yaquina River and Big Elk Creek). 


7 















90- 


w 60- 


co 


0 

O) 
v_ 

CD 
.£= 

O 
CO 

Q 30-1 


0 


F-q 



“I-1-1-1-1-1-1-1-!-1-1-1— 

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

Month 


Figure 2.3 Monthly discharge statistics (Yaquina River + Big Elk Creek) calculated using data 
from 1972-2002 Chitwood gauge (corrected for Big Elk Creek using relationship from 
Brown and Ozretich, in review). In the plot, the boxes represent the 25 th and 75 th 
percentiles, the whiskers represent the 5 and 95 percentiles, and the horizontal line is 
the median. The dashed line indicates 30-year median discharge. 


Estuaries in the Pacific Northwest (PNW) are adjacent to the California Current System, 
which exhibits strong interannual, seasonal and event scale variability (Hickey and Banas, 2003). 

In this region, seasonal wind-driven upwelling advects relatively cool, nutrient rich (NO3 and 

3 - 

P0 4 ) water to the surface. The upwelling season typically commences in April and continues 
through September, approximately coinciding with the dry season. During this time period, 
upwelling favorable winds from the north dominate. The upwelling conditions are interrupted by 
brief periods of downwelling favorable conditions, which usually persist for several days. 
Previous studies have demonstrated that the oceanic inputs of nutrients and phytoplankton are 


8 



















































important for estuaries adjacent to coastal upwelling regions, such as the west coast of the United 
States (e.g., de Angelis and Gordon, 1985; Roegner and Shanks, 2001; Roegner et al., 2002; 
Colbert and McManus, 2003; Brown and Ozretich, in review). 

2.2 Biotic Characteristics 

PNW estuaries, including Yaquina Estuary, are highly productive ecosystems, supporting 
several hundred species of macrophytes, macroinvertebrates, fish, birds, marine and terrestrial 
mammals. The Yaquina estuarine ecosystem contains six major habitats, three of which are 
defined by the presence of an ecosystem-engineering species: tidal channels (including water 
column and subtidal unvegetated sediments), eelgrass beds (lower intertidal and shallow subtidal 
sediments dominated by Zostera marina), mud shrimp beds (mid- to lower intertidal muddy 
sediments dominated by Upogebia pugettensis ), ghost-shrimp beds (lower- to upper intertidal 
sediments dominated by Neotrypaea californiensis), unvegetated intertidal sediments, and tidal 
marshes. Each habitat supports different floral and faunal communities (Seliskar and Gallagher, 
1983; Simenstad 1983; Phillips, 1984; Ferraro and Cole, 2006). 

One hundred twenty-eight species of macroalgae (Kjeldsen, 1967) and three species of 
seagrass have been recorded in Yaquina Estuary. The species diversity and biomass of 
macroalgae is greatest near the mouth of the estuary and decreases up river (Kjeldsen, 1967). 
From late spring to early fall, green macroalgae (principally Enteromorpha spp. [6 spp.], Ulva 
spp. [6 spp.], and Cheaetomorpha spp. [2 spp.]) form extensive intertidal and shallow subtidal 
mats in the lower portion of the estuary, but are largely absent upstream of Poole Slough (about 
11 km from mouth; Figure 2.4). Above Toledo, macroalgae diversity declines to <5 spp. and 
biomass is negligible (Kjeldsen, 1967; WED unpublished data). Large meadows and long 
patches of the native seagrass, Zostera marina (eelgrass), occur on the intertidal flats and along 
channel edges in the lower estuary. From Poole Slough upriver to Toledo, eelgrass occurs 
sporadically in the shallow subtidal, with the largest patches occurring near the Toledo public 
boat launch, which is about 18 km from the mouth of the estuary (Figure 2.4). The introduced 
seagrass, Z. japonica, occurs in the upper-to-mid intertidal zone from the lower estuary to 
Toledo, occasionally forming large beds; its abundance is increasing and it may eventually 
compete for space with the native eelgrass. Widgeon grass ( Ruppia maritima ), the third seagrass 


i i- 


9 


species, occurs in small, isolated patches, but is uncommon relative to the other seagrasses 
(Bayer, 1996). 

Recent surveys identified over 168 species of macroinvertebrates in Yaquina Estuary, 
with diversity and biomass highest in the lower estuary and lowest in the upper estuary (WED 
unpublished data). Polychaetes are the most numerous macroinvertebrate taxa, but ghost and 
mud shrimp (Neotrypaea cahforniensis and Upogebia pugettensis ) dominate the infaunal 
biomass (WED unpublished data). Bioturbation, bioirrigation, and feeding activities of these 
shrimps accelerate carbon and nutrient cycling within the estuary, and enhance the flux of 
dissolved nitrogen from sediments to the water column (DeWitt et al., 2004). Deposit and filter 
feeders are the most abundant benthic consumers, with filter feeders, primarily mud shrimp, 
dominating in the lower estuary. As mud shrimp abundance declines up-estuary, deposit feeders 
become more abundant. 

Five species of bivalves (cockle [ Clinocardium nuttali ], soft-shell clam [ Mya arenaria], 
littleneck clam [ Venerupis staminea ], gaper clam [Tresus capax ], and butter clam [, Scixidomus 
giganteus ]) are harvested recreationally, primarily in the lower portions of Yaquina Estuary. 
Although commercial harvest of these species is currently allowed, there have been no 
significant landings since the mid-1990's. Prior to that time, commercial landings, varied 
between 1,000 and 8,000 lbs. per year (P.M. Vance, Oregon Department of Fish and Wildlife 
(ODFW), personal communication). Non-native Pacific oysters (Crassostrea gigas) are grown 
commercially on 519 acres of leased tidelands in the middle reach of Yaquina Estuary, near 
McCaffrey and Poole Sloughs, with an annual production of 15,028 bushels valued at $594,000 
(Oregon Department of Agriculture 2000-2005). This equates to 45% of the Oregon commercial 
oyster production on state-owned tidelands. 

At least 62 species of finfish and epibenthic crustaceans occur in Yaquina Estuary, with 
the highest diversity and abundance found in the lower estuary, and reduced diversity and 
abundance upriver (DeBen et ah, 1990). Fish and crustacean abundance and diversity is highest 
during summer and lowest in winter. Estuary-wide, English sole [ Parophrys vetu/us\, Pacific 
snake blenny [Lumpeniis sagitta ], and shiner sea perch [Cymatogaster aggregate ?] are the three 
most abundant fishes, and sand shrimp [Crangon spp.], dungeness crabs [ Cancer magister ], and 
mysids [Neomysis mercedis ]) are the three most abundant epibenthic crustaceans (DeBen et ah, 
1990). Of these, dungeness crabs have the greatest economic value, supplying recruits to the 


10 


offshore commercial crab fishery (Armstrong et al., 2003) and adults to the within-estuary 
recreational fishery. Annually, approximately 75,000 dungeness crabs are harvested in the 
recreational fishery in Yaquina Estuary (P.M. Vance, ODFW, personal communication). Fifteen 
species of fish account for >90% of the fish caught recreationally for food or bait from Yaquina 
Estuary (PSMFC, 2006). Statewide, lower-estuary recreational finfish fishing contributes $18.8 
million to Oregon’s economy (The Research Group 2005). Estuary-specific estimates for the 
recreational fishery’s value are not available, but recreational salmon fishing in Newport-area 
estuaries (i.e., predominantly Yaquina Estuary) contributes $4.05 million to the State economy 
(The Research Group, 2005). The Yaquina watershed and estuary support breeding populations 
of five salmonid species (chinook salmon \Oncorhynchus tshawytscha], coho salmon [ O . 
kisutch ], chum salmon [O. keta ], steelhead [O. my kiss], and cutthroat trout [O. clarki clarki ]), 
including the southern-most population of chum salmon in North America (Bob Buckman, 
ODFW, personal communication). Coho salmon are being considered for special conservation 
status because of reduced population size in Yaquina and other Oregon mid-coast estuaries 
(ODFW, 2006). The only commercial finfish fishery within the estuary is for Pacific herring 
(Clupea pallasii pallasii ), whose ovaries and roe are marketed to Asia, with an annual average 
(1979-2006) landing of 153,300 lbs, valued at $76,300 (Keith Matteson, ODFW, personal 
communication). 

Thousands of birds live in or migrate through Yaquina Estuary, which is designated as a 
Continental Important Bird Area (IBA) by the American Bird Conservancy and as a State IBA 
by the National Audubon Society. Two hundred-sixteen species of birds have been observed 
during 1994-2006 Christmas Bird Count surveys (National Audubon Society, 2002). Sixty- 
seven species of waterbirds were censused during 1993-1994 in the estuary, of which 41 were 
year-round or seasonal residents; maximum diversity and abundance occurred in December, and 
was at minimum in June (Merrifield, 1998). 

Small populations of Pacific harbor seals (Phoca vitulina richardsi ) and California 
sealions ( Zalophus californianus) are present year-round in Yaquina Estuary, feeding on fish and 
crabs in the lower estuary (Orr et al., 2004; Brown et al., 2005). Killer whales ( Orcinus orca ), 
gray whales ( Eschrichtius robustus), and harbor porpoises ( Phocoena phocoena) occasionally 
and briefly enter the lower estuary. Other common mammals in the tidal portions of Yaquina 
Estuary include river otter ( Lutra Canadensis ), raccoons (Procyon lotor ), muskrat ( Ondatra 


> ^ 


11 



Newport 




5 km 


Toledo 


+ 


0 km 

+ 


Sallys 

Bend 


20 km 


+ 


10 km 


+ 


15 km 


+ 


in "O 
« o 


1 "1 _ 

1 

1 km 

0 0.5 1 

2 

3 


Seagrass 

Macroalgae 

Intertidal 

Subtidal 


Figure 2.4 a) False-color, infrared aerial photography mosaic of Yaquina Estuary (taken in 
1997) and b) map of intertidal seagrass and macroalgae in Yaquina Estuary classified 
from image analysis (WED unpublished imagery). 


12 



























zibethicus ), and nutria ( Myocastor coypus), particularly in low-salinity tidal marshes (USFWS, 
1968). River otter hunt for fish in tidal channels, raccoons forage for molluscs and crustaceans 
on tide flats, whereas muskrats and nutria feed on marsh plants (Howerton, 1984). Over 70 other 
mammals are reported from Lincoln County watersheds, many of which use wetland and 
terrestrial habitats bordering the estuary (National Wildlife Federation eNature ZipGuides 
website: http://www.enature.com/zipguides/). 

2.3 Land Use and History of Anthropogenic Modifications 

The Yaquina watershed covers an area of 650 km , tapering towards the mouth of the 
Yaquina Estuary but extending about 35 km inland. The watershed contains the city of Toledo, 
however, most of the city of Newport with the exception of the “Bay Front” lies outside of the 
watershed boundaries (Figure 2.2). The total population in the Yaquina watershed in 2000 was 
approximately 7970 or 12.3 persons per km" (source: Lee et al., 2006). The population density 
in the Yaquina watershed is similar to other PNW estuarine watersheds (mean =15 persons per 
km 2 , Lee et al., 2006), and is much lower than the national average for the coastal region of the 
United States (mean =116 persons per km ; Crossett, et al., 2004). The population trend in the 
Yaquina watershed differs from many coastal watersheds in the United States in that the 
population in the Yaquina watershed declined by 4.8% from 1990 to 2000. Population changes 
during the interval of 1980 to 2000 in PNW coastal watersheds (excluding Puget Sound and 
Columbia River) are among some of the lowest in the United States (Crossett, et al., 2004). 
Utilization of the Yaquina Bay Front increases substantially with the influx of tourists during the 
summer. 

Historical population data on a watershed basis are not available before 1990 because the 
census block data are not available in a GIS format to allow proration of the population by 
watershed boundaries. However, it is possible to track the historical population changes in the 
cities of Newport and Toledo (Figure 2.5). The major population center in the Yaquina 
watershed is the city of Toledo, which accounted for 44% of the population in the watershed in 
2000. Toledo has experienced low growth, increasing by 12% from 1960 to 2005. In contrast, 
Newport has grown steadily, increasing by 84% from 1960 to 2005. While most of the city of 
Newport lies outside of the Yaquina watershed, the increase in population, as well as an increase 


' f 


13 


in tourism over this period, reflects an increase in utilization of the Yaquina Estuary through 
recreational boating, recreational fishing, and utilization of bay-side restaurants and facilities. 

The Yaquina watershed is heavily forested with deciduous, evergreen and shrub land use 
classes constituting 85% of the watershed (Lee et al., 2006 based on NOAA 2001 C-CAP data 
( www.csc.noaa.gov/crs/lca/ccap.html )). Grasslands constitute 6% of the watershed while high 
and low intensity development combined only constitute 0.5% of the watershed. While 
developed areas constitute a small percentage of the watershed, they are increasing with the high 
residential and low residential land use classes increasing by 4.5% and 6.8%, respectively, from 
1995 to 2001. Reflecting the low extent of development, the percent impervious surface is only 
2.4% (Lee et al., 2006). As is typical of coastal watersheds in the PNW, the Yaquina watershed 
is “rugged” with a median slope of 29.7 percent (16.5 degrees). 



14 






Although primarily forested and showing little “urban footprint”, the Yaquina watershed 
has been impacted by a variety of disturbances during the last century, in particular fires and 
logging. The largest fire occurred in 1853 when the “Yaquina Bum” consumed 1942 km 2 of 
coastal forest from near Corvallis to Yaquina Estuary. Logging of the coast range began in the 
mid-1800’s and extensive logging of Sitka spruce occurred along the coast during World War I, 
much of it centered near Toledo 

( www.ohs.org/education/oregonhistory/narratives/subtopic.cfm7subtopic ID=76 ). Logging has 
continued within the Yaquina watershed to the present, and forest lands are the dominant land 
use within the watershed, accounting for 566 km (90%) of the land zoning area in the Yaquina 
Basin (area = 639 km 2 ; Garono and Brophy, 2001). While the intensity of logging varies with 
economic trends and the age and marketability of the standing timber, it is not uncommon to see 
patches of clear cut forest within the Yaquina watershed. 

In addition to the direct effects of logging on erosion and water quality, rafting of logs 
can potentially affect freshwater and estuarine habitats by physical disturbance, altering flow 
regimes, and accumulation of wood and bark debris which in turn can smother the benthos and 
result in low dissolved oxygen and/or elevated FLS (Sedell et al., 1991). During the early 1900s 
until the 1980s, the estuaries and streams of the PNW were used for the transport and storage of 
logs (Sedell and Duval, 1985). Logs have been rafted in the Yaquina since at least 1920, with a 
substantial increase after the construction of the Georgia Pacific West mill in 1957 in Toledo 
(Figure 2.6). Peak abundance of rafted logs occurred in 1962, and log rafts declined through the 
early 1980s with the increase in environmental regulation and changes in markets (Figure 2.6); 
Sedell and Duval, 1985). In addition to the bark debris, accumulation of sawdust has also been 
observed in the estuary (Kulm and Byme, 1966). 

In addition to log rafting, three other sources of biological oxygen demand (BOD) in the 
Yaquina Estuary are sewage from municipal discharges, industrial discharges, and non-point 
inputs, in particular from septic systems. As was common for the period, untreated sewage and 
industrial waste from Toledo and the Newport bay front were discharged directly into the 
Yaquina Estuary in the 19 th century and the first half of the 20 th century. Sufficient untreated 
sewage and other wastes were discharged that they represented a potential health hazard for the 
oysters grown in the bay in the first quarter of the 20 century (Fasten, 1931). 


' i- 


15 




Figure 2.6 Number of logs floated or rafted on the Yaquina River from 1918 to 1978 
(reproduced from Sedell and Duval, 1985). 

A combined sewage discharge with a pump station was constructed for Newport in the mid- 
1950s, which eliminated the direct discharge of sewage from Newport into Yaquina Estuary (Lee 
Ritzman, City of Newport, personal communication). A municipal sewage system with primary 
treatment and an offshore discharge was constructed in Newport in 1964, which has since been 
upgraded to secondary treatment. A combined stormwater/sewage system that discharged raw 
sewage into the Yaquina River was constructed in Toledo in 1926, and then upgraded in 1954 to 
a primary treatment facility to handle the municipal waste from the city of Toledo (T. 
McFetridge, ODEQ, personal communication). This facility, which discharges into the Yaquina 
Estuary (about 22 km from the mouth of the estuary), was upgraded to secondary treatment in 
1981. In the late 1980’s and early 1990’s, the City of Toledo made improvements to their 
stormwater collection system, reducing the bypassing of the treatment plant during high flow 
periods. In 1996, the Toledo plant had a discharge of 0.979 million gallons per day (MGD) with 
a design capacity of 3.5 MGD ( www.epa.gov/OW-OWM.html/mtb/cwns/1996report2/or.htm ). 

In addition to the Toledo municipal discharge, a number of houses along the Yaquina 
Estuary and River have on-site septic systems. The primary environmental impact of these 
septic systems appears to be microbial contamination which primarily affects the oyster industry 
in Yaquina Estuary. The lower portion of the Yaquina Estuary is impaired for shellfish growing 


16 







due to fecal coliform (Table 1.1). Due to concern for microbial contamination associated with 
human and animal waste, a survey of residential septic systems was conducted during 1985- 
1986. Septic systems for 160 residences adjacent to the Yaquina Estuary were surveyed and it 
was found that approximately 17% of the residences surveyed had marginal septic systems and 
16% had failing systems. The failing systems identified have since been corrected (Bill Zekan, 
Lincoln County Oregon, Planning and Development, personal communication). 

There are three types of industrial discharges into Yaquina Bay/River. Six seafood 
processing plants discharge waste into Yaquina Bay 

(http://www.deq.state.or.us/wq/sisdata/sisdata.asp), all of which are classified as “minor” by the 
ODEQ. Though relatively small discharges, two of the companies have been fined by ODEQ for 
violating their permits. The Yaquina Bay Fruit Processors also discharges brine waste into 
Yaquina Estuary. The third type of discharge is waste from the Georgia Pacific West kraft pulp 
and linerboard mill. The mill went into production in 1957 with the primary discharge through 
an ocean outfall offshore of Newport. There is an emergency overflow outfall (located about 21 
km from the mouth of the estuary) that discharges directly into the Yaquina Estuary; however, 
this outfall has discharged only ten times from 1999 to 2004, with a maximum discharge of 0.24 
MGD. The discharges typically occur during heavy rain events for short time periods (less than 
24 hours). 

2.4 Classification of the Yaquina Estuary 

Classification has been proposed as an important tool for developing nutrient criteria for 
estuarine systems (e.g., U.S. EPA 2001). Classification of estuaries in terms of their 
susceptibility to nutrient enrichment is theoretically highly desirable because of the large number 
of estuaries in the United States and limited resources, which make it unfeasible to develop 
nutrient criteria on a case by case basis for each individual system. Numerous types of estuarine 
classifications have been developed or proposed, including ones based on geomorphology, 
physical and hydrodynamic factors, and susceptibility to nutrient enrichment (Kurtz et ah, 2006). 
A key aspect of the use of any classification system for setting nutrient criteria is that estuaries 
within the same class respond similarly to nutrients, which is a step that must be validated and 
has not yet been accomplished for national scale estuarine classifications in the U.S. 


t t 


17 


Several estuarine classifications have included Yaquina Estuary. Bottom et al. (1979) 
classified Yaquina Estuary as a “Drowned River Valley” and partially mixed estuary. NOAA 
classified Yaquina Estuary as “River Dominated” with “Straits and Terminal Bay.” Quinn et al. 
(1991) classified estuaries along the west coast of the United States based on their susceptibility 
to nutrient pollution. In this study, they classified the Yaquina Estuary as in the high category 
for dissolved concentration potential (DCP) and in the low category for particle retention 
efficiency. They estimated that the nutrient concentration for nitrogen and phosphorous would 
be in the medium class based on DCP and estimates of nutrient loadings. Additionally, Quinn et 
al. (1991) estimated that Yaquina Estuary would require > 20% increase in nutrient loading to 
change the concentration from medium to high class. Burgess et al. (2004) classified estuaries in 
the U.S. based on a statistical cluster analysis of physical and hydrologic factors. They classified 
Yaquina Estuary as a “Medium Area, Low Volume, Shallow and Mixed Salinity” estuary. 

2.5 Conceptual Model for Yaquina Estuary 

Figure 2.7 illustrates some of the major drivers influencing causal (nutrients) and 
response (chlorophyll a, water clarity and dissolved oxygen) variables within the Yaquina 
Estuary, which will be presented in this case study. Nutrient, chlorophyll a , and dissolved 
oxygen conditions in the lower portion of the estuary are strongly influenced by ocean conditions 
due to close coupling between the shelf and the estuary resulting from strong tidal forcing. The 
watershed is primarily forested, and riverine inputs are related to the presence of nitrogen-fixing 
red alder (Alnus rubra) trees in the watershed. Seagrasses occur at shallower depths in the upper 
portions of the estuary than they do in the lower estuary, which we believe is related to increased 
turbidity upriver and the resulting light limitation. Dense macroalgal blooms occur in the lower 
portion of the estuary, but they appear to be fueled by oceanic nitrogen inputs rather than being a 
response to anthropogenic nutrient enrichment. 


18 


<D 



<u 

u 

.1 

Uh 





3. Description of Sources/Sinks of Nutrients 


3.1 Background 

In most estuaries, the major sources of nitrogen are atmospheric deposition, agricultural 
nitrogen fixation, fertilizer runoff, animal feeding operations runoff, and in heavily populated 
areas point source inputs associated with wastewater treatment facilities (WWTF) (Driscoll et al., 
2003; Howarth et al., 2002; Boyer et al., 2002). For many PNW estuaries (with the exception of 
Puget Sound), there is relatively low population density in the watersheds and low atmospheric 
nitrogen deposition. The watersheds are predominantly forested, resulting in low nitrogen inputs 
associated with fertilizer and agriculture nitrogen fixation. Upwelling provides a major source of 
nutrients to estuaries adjacent to coastal upwelling regions, such as the PNW (e.g., Hickey and 
Banas, 2003 and Brown and Ozretich, in review). Low intensity landuse and coastal upwelling 
result in a significant difference in dominant sources of nutrients to PNW estuaries compared to 
estuaries elsewhere in the U.S. 

In a recent review, Tappin (2002) found that the input of nitrogen to temperate and tropical 
estuaries from the ocean is poorly quantified. It is important to quantify the contribution of 
oceanic input to nutrient loading in order to determine background conditions for estuaries that 
are adjacent to upwelling regions and to distinguish natural variability from anthropogenic 
inputs. We also do not know how susceptible estuaries subjected to large oceanic inputs of 
nutrients (dissolved inorganic nitrogen and phosphorous) are to future changes in anthropogenic 
inputs of nutrients. Addressing issues associated with ocean input of nutrients is critical in the 
process of developing nutrient criteria for estuaries in the PNW region. 

3.2 Nitrogen Loading to Yaquina Estuary 

Brown and Ozretich (in review) compared the sources of nutrients to Yaquina Estuary 
during the wet and dry seasons (Table 3.1). There are large seasonal differences in the sources 
of nitrogen to the estuary. During the wet season, riverine sources dominate, while during the 
dry season oceanic nitrogen inputs associated with coastal upwelling dominate. In the dry 

season, benthic flux of dissolved inorganic nitrogen (DIN= NO 2 + NO 3 +NH 4 ) from the 
sediments into the water column is the second largest source of DIN. Atmospheric deposition of 
inorganic nitrogen along the central Oregon coast is among the lowest in the United States. 


20 


Average annual deposition during 1980-2002 was 0.6 kg N ha ' 1 y ' 1 (NADP, 2003). Atmospheric 
deposition of nitrogen is a minor component of nutrient input to Yaquina Estuary with direct 
deposition on the estuary only representing 0.05% of the nitrogen input to the estuary. 
Atmospheric deposition on the watershed is a small source ( 8 %) compared to the watershed 
input associated with nitrogen-fixing red alder trees in the watershed (Brown and Ozretich, in 
review). Annual input of nitrogen from WWTF effluent is estimated to be 0.4% of the total 
nitrogen input to the estuary. A NOAA study of estuarine susceptibility to nutrients (Quinn et 
al., 1991) estimated point source loading to Yaquina Estuary as about an order of magnitude 
higher than our estimates. 

3.2.1 Watershed 

There is approximately an order of magnitude difference in the 30-year average daily 
riverine nitrogen input to Yaquina Estuary between the wet and dry seasons. In addition, there 
are considerable interannual differences in riverine nitrogen input, with wet season riverine 
nitrogen input varying from 6.5 x 10 4 mol N d ' 1 to 5.2 x 10 5 mol N d 1 , and dry season riverine 
nitrogen input ranging from 1.1 x 10 4 mol N d _1 to 6.3 x 10 4 mol N d ' 1 (Brown and Ozretich, in 
review). During the wet season, riverine input is the largest source of DIN to the estuary, 
contributing approximately 78% of the input, while 91% of the annual riverine nitrogen input is 
delivered during the wet season. Our estimates of riverine nitrogen loading (Table 3.1) are 
similar to Quinn et al. (1991) whose estimate of non-point loadings are 7% higher than our 
estimate of annual riverine loading. Sigleo and Frick (2007) estimated that the annual riverine 

nitrate (NO 3 ) input to Yaquina varied from 2.4 x 10 5 mol N d ' 1 to 5.2 x 10 4 mol N d ' 1 during a 
drought year. 

Oregon Coast Range streams have high NO 3 concentrations relative to other forested 
watersheds in the PNW (Compton et al., 2003; Wigington et al., 1998). Wigington et al. (1998) 
hypothesized that forest vegetation, in particular the presence of red alder, is the primary factor 

determining stream N0 3 levels in the Oregon Coast Range. Red alder is a native tree species in 
the PNW that colonizes areas disturbed by fires, logging and landslides. Red alder have 
symbiotic N 2 fixing bacteria that can fix 50-200 kg N ha ' 1 y ' 1 in pure stands (Binkley et al., 

1994). Compton et al. (2003) found a significant relationship between alder cover and stream 

NO 3 concentration in the Salmon River watershed, which is about 45 km north of Yaquina 

21 


1 1- 


Estuary. Naymik et al. (2005) found a similar relationship between stream total nitrogen and 
broadleaf cover (which is primarily red alder in the Coast Range) in the Tillamook watershed. In 
the Yaquina Estuary watershed, 23% of the watershed is vegetated with red alder (Brown and 
Ozretich, in review). Brown and Ozretich (in review) estimated that > 80% of the riverine 
nitrogen loading to Yaquina Estuary is related to red alder cover. Thus, riverine nutrient loading 
in the PNW is influenced by forest species composition. 

3.2.2 Ocean Input 

Brown and Ozretich (in review) estimated oceanic input of DIN to the Yaquina Estuary 
during the dry season of 2002 and 2003. The oceanic input of DIN was calculated using the 
time-series of flood tide input of DIN multiplied by the volume of water entering the inlet during 
each tidal cycle. The volume of water entering the inlet was calculated using a two-dimensional, 
laterally averaged hydrodynamic and water quality model (described in Brown and Ozretich, in 
review). Daily water samples were collected during flood tide approximately 0.5 m below the 
surface at a station about 3.7 km from the mouth of the estuary. These samples were analyzed 

+ 3 - 

for dissolved inorganic nutrients (NO3 + NO2, NH4 , PO 4 and Si(OH) 4 ). During the dry season 
of 2002, the amount of DIN entering the estuary from the ocean during each flood tide varied 
from 8.8 x 10 3 mol N to 6.7 x 10 5 mol N with a mean value of 2.4 x 10 5 mol N, and the mean 
daily flood tide input of DIN was 4.7 x 10 5 mol N d 1 . During the 2003 dry season, the mean 
oceanic input of DIN is 3.7 x 10 5 mol N d " 1 or 21% less than 2002 dry season. Sigleo et al. 

(2005) calculated the flood tide input of NO3 to Yaquina Estuary during August of 2000 to be 13 
x 10 5 mol N d’ 1 , which is about triple our estimate. However, these ocean input numbers were 

calculated using a constant flood tide NO 3 of 30 pM. 


22 


Table 3.1 Comparison of the magnitude of nitrogen sources during wet and dry seasons for 
Yaquina Estuary, Oregon. Benthic flux measurements not available (NA) for wet season. 


Source 

Wet Season 

Nitrogen Input (mol N d' 1 ) 

Dry Season 

Annual Average 

River 

2.7 x 10 3 

2.5 x 10 4 

1.4 x 10 3 

Ocean 

3.0 x 10 4 

3.7-4.7x I0 5 

2.3 x 10 5 

Wastewater 

1.7 x 10 3 

1.5 x 10 3 

1.6 x 10 3 

Benthic Flux 1 

NA 

4.3 x 10 4 

NA 

Atmospheric Deposition 2 




On Estuary 

2.2 x 10 2 

1.2 x 10 2 

1.7 x 10 2 

On Watershed 

1.1 x 10 4 

6.0 x 10 3 

8.5 x 10 3 

Source: 'DeWitt et al. (2004); 

2 NADP (2003) 




3.2.3 Benthic Processes 

Intertidal and subtidal sediments can be sources and sinks for nutrients and organic 
matter, with the direction and magnitude of fluxes determined by infaunal invertebrates, benthic 
primary producers, and microbial communities living on or in the estuarine benthos. (See 
Appendix A for additional details on benthic processes). 

Five studies of benthic nutrient flux have been conducted in Pacific estuaries north of San 
Francisco, however the reported benthic flux data in four of the studies (i.e., Dollar et al. 1991; 
Garber et al. 1992; Thom et al., 1994; Lamed, 2003) may not accurately estimate estuary-scale 
nutrient fluxes in Yaquina Estuary because they do not account for the presence of thalassinid 
burrowing shrimp. The presence of burrowing shrimp can result in the water inside of the 
benthic flux chamber being exchanged with water outside of the chamber via shrimp burrows 
(e.g., Hughes et al., 2000), which violates the requirement that benthic chambers be closed 
microcosms (Forja and Gomez-Parra, 1998). 

To avoid this problem, DeWitt et al. (2004) inserted 1-m deep core barrels into sediments 
at their study sites, and fit benthic chambers to the tops of the core barrels to isolate water, 
sediments, shrimp and burrows inside the chamber from the outside world. DeWitt et al. (2004) 
demonstrated that DIN efflux was strongly affected by both burrowing shrimp species and 
population density (Appendix A). Integrated over the whole estuary, net DIN efflux for 
intertidal habitats in Yaquina Estuary was 4.3 x 10 4 mol N d~' from the benthos to the water 
column (DeWitt et al., 2004). (Additional details on composition of estimated DIN efflux 
provided in Appendix A) 


11- 


23 




3.3 Zonation Based Upon Nitrogen Sources 

We divided the estuary into two zones, one of which is dominated by ocean input 
(Zone 1) and the other which is more influenced by watershed and point source inputs (Zone 2, 
Figure 2.1). We used a transport model combined with natural abundance stable isotopes (5 I5 N) 
of green macroalgae to identify the dominant nitrogen sources within the estuary as a function of 
time and location for two years (2003 and 2004). The transport model was validated by 
comparing predicted isotope ratios (using the transport model to mix isotopic end members) to 
observed macroalgal isotope ratios at five locations. For more details on this analysis, see 
Chapter 5 of Lee et al. (2006). 

Model simulations combined with 8 I5 N of green macroalgae suggest that during the wet 
season, riverine nitrogen sources dominate throughout the estuary, which is consistent with our 
comparison of nutrient loadings presented in Section 3.2. During the dry season, ocean nitrogen 
sources dominate in Zone 1, comprising between 53 - 87% of DIN (depending upon location 
within the zone), whereas riverine and WWTF inputs contribute 12-40% and 2-8%, respectively. 
In Zone 2, riverine nitrogen sources dominate contributing between 56-92% of DIN (depending 
upon location). WWTF contribution to water column DIN is maximal during the month of 
August. 

During the dry season, oceanic input of nitrogen propagates up estuary as the freshwater 
inflow declines. This can be seen in simulation results from 2004 (Figure 3.1) which show that 
Station N1 is ocean dominated (fraction > 0.5) during the entire dry season (May - September), 
while Station N2 is river dominated during May and ocean dominated from June - September. 

At Stations N3, N4, and N5 ocean inputs increase in importance from May - August, but never 
dominate. There is interannual variability in the position of the line demarking the oceanic and 
riverine dominated zones. The exact location of this line varies with ocean conditions (e.g., El 
Nino, La Nina conditions) as well as freshwater inflow. To be conservative, we placed the line 
demarking the two zones at the most seaward location found in our analysis (see Figure 2.1 for 
location). Analysis of salinity data reveals that the demarcation of the two zones corresponds to 
a dry season median salinity of 26. 


24 


120i . | 120i . | 120 r . i 120i . i 120 


LO 





03 

00 

N- 

CO 

in 

d- 

co 

CM 


o 

00 


o 

d - 


£ C 

t Jr CD 

> .> o 

^ a: O 

IDS 


o 

00 


o 

d - 



IAI >1 ‘NIQ 



: 






CO 

o 


co 

o 


d 

o 


CM 

o 


UOjPBJJ 


& 

d 

d 

c 

d 





03 

a> 

fcf) 

d 

‘C 

d 

Pj 

c/3 

<L) 

00 

d 

TD 

5 

C/3 

<L> 

r— 

N- 

b 

<D 


CO 

a-) 

<+h 

03 


O 

uo 

s 

4-^ 



C/3 

<+H 

M—» 

d" 

<D 

O 

P 

CO 

<L> 

_ r* f 

o 

CM 

r] 

4—» 

£ 

M—* 

P 


_ 

c 

O 

a> 


CO 

C 


<D 

O 

s- 

d 

o 

C/3 

a 

c 

03 

CD 

O 

o 

-a 

c 

d 

<D 

c 

'C 

<u 

> 



X 

o 

03 

c 

00 

o 

■4—4 

h- 

d 

CO 

X 

‘h 

in 

■4—4 

c 

■d 

o 

o 

co 

T3 

CM 

JD 

T ” 

'd 

-d 

O 


m 

<D 

u. 

d 

6X) 

• 

X 


<u 

X 


03 

00 

d 

a 

4—* 

S 

o 

l-- 

o 


co 

in 

£ 

in 

■4—4 

d 

XI 

d 

Z 


co 


o 

5 


CM 

o 

-d 


4—♦ 

C/3 

c 

d 


r- 

cn 

-4— 

d 

o 

X 

03 

CD 

c 

o 

N 

d 

"d 

<d 
- 1 —> 
03 
O 

o 

<D 

H 

03 

CM 

z 

'O 

c 

03 


C/3 

c 

_o 

-4—> 

d 

ori 

d- 

o 

o 

CM 

X 
O 

J- 

CD 

XI 

6 

<u 

4—* 

a. 

<d 

C/0 


o 

£ 


X 

o 

X 

CM 

TD 

C 

03 

r\ 

d" 

oo 

X 

<n 


d 

o 

x 

03 

CM 

CD 

C 

o 

N 


T3 

<D 

- 4—4 

d 

o 

<D 

!- 

d 

CC3 

z 

’d 

c 

d 

c\ 

d - 

z 

r\ 

m 

z 

C/3 

c 

o 

■M 

d 

--> 

C/3 


CD 

> 

- 4 —* 

o 

a/ 

a. 

C/3 

<d 


IT) 

CM 


'/ 





































































































































































4. Data Sources and Methods 

We assembled causal (nutrient) and response variables (chlorophyll a , dissolved oxygen, water 
clarity, total suspended solids, macroalgae biomass, and submerged aquatic vegetation distribution) 
and physical data (temperature and salinity) at three spatial scales. Water quality data from Yaquina 
Estuary was compared to water quality data collected during the dry season for a set of seven Oregon 
estuaries for the purpose of estuarine classification (Section 4.2), and from a random sampling of all 
Oregon estuaries conducted as part of the EPA National Coastal Assessment (NCA) (Section 4.3). 
Historical data were assembled to assess whether there have been any long term trends in causal or 
response variables. For the trend analyses, we parsed the data into zones and seasons to minimize bias 
associated with differences in sampling (temporal or spatial). The zones are presented in Figure 2.1 
and discussed in Section 3.3, while the seasons are defined in Section 2.1. For details on the methods 
used and the quality assurance/quality control (QA/QC) of data used in this study see Appendix B. 

4.1 Yaquina Estuary Data 

4.1.1 Recent Data 

Data assembled for the Yaquina Estuary included recent (1998-2006) water quality cruises 
conducted by the Western Ecology Division, U.S. EPA. The sampling frequency and number of 
stations depended upon the year and month (Table 4.1). At each station, profiles of conductivity, 
temperature and depth (CTD; SBE 19 SEACAT Profiler, Sea-Bird Electronics, Inc, Bellevue, 
Washington), turbidity (Seapoint Turbidity Sensor, Seapoint Sensors, Inc., Kingston, New Hampshire), 
in situ fluorescence (WETStar Chlorophyll Fluorometer, WET Labs, Philomath, Oregon), and 
photosynthetically active radiation (PAR; PAR LI-193 underwater irradiance sensor, Lincoln, 
Nebraska) were measured. The profile measurements were taken at 0.5-sec intervals from the water 
surface to 0.5 m above the bottom, and during post-processing the data were binned into 0.25-m 
intervals. For the cruises conducted in 2006, dissolved oxygen was measured at surface, mid-depth, 
and bottom using a YSI multiparameter sonde (YSI 6600 EDS, YSI Inc., Yellow Springs, OH). At 

each station, water samples were collected, which were analyzed for dissolved inorganic nutrients 

- - + 3- 

(N0 3 + N0 2 , NH 4 , P0 4 and Si(OH) 4 ). During the 2006 cruises, additional water samples were 
collected and analyzed for total nitrogen (TN) and total phosphorous (TP). Water samples were 
collected for chlorophyll a analysis at each cruise location quarterly during 2002 and 2003 (surface 


26 


samples), and monthly during 2006 (mid-depth samples). Additional water samples were collected for 
total suspended solids (TSS) analysis. 

Light attenuation coefficients (kj) were determined for each station as the slope of the regression 
of In (PAR) vs. depth for the 1.00 m to 3.75 m depth intervals. Many of the light profiles measured 
during the cruises were conducted during flood tides; therefore, the light attenuation coefficients may 
be biased toward clearer flood tide conditions. In addition to the cruise data, PAR was monitored 
continuously with 15 minute averages recorded at five locations in the estuary (WED unpublished 
data). Three of these sites were in Zone 1 (located 3.7, 3.9, and 9.0 km from the mouth of the estuary) 
and two were in Zone 2 (located 18.4 and 16 km from the mouth of the estuary). Measurements at 
these sites were taken nearly continuously from 1999 through 2003 using two PAR sensors placed 0.75 
m apart in depth, which were used to calculate light attenuation coefficients. The sensors were cleaned 
at one to two week intervals. For the analyses presented in this document, we used attenuation 
coefficients measured at local noon time and within 4.5 days of cleaning for the continuous data set. 

Additional high temporal resolution data were collected at the riverine and oceanic boundaries to 
quantify the oceanic and riverine inputs of dissolved inorganic nutrients and chlorophyll a to the 
estuary. Continuous data (including water temperature, salinity, dissolved oxygen, and in situ 
fluorescence at 15-min intervals) from YSI multiparameter sondes (YSI 6600 EDS, YSI Inc., Yellow 
Springs, OH) were available at approximately six locations in the estuaries (with the exact number of 
locations depending upon the year and month). 

4.1.2 Additional Data Sources 

A summary of historic data compiled for the Yaquina Estuary is provided in Table 4.2. In 
addition to the sources listed in Table 4.2, data were obtained from the Oregon Department of 
Environmental Quality Laboratory Analytical Storage and Retrieval (LASAR) database 
(http://deq 12.deq.state.or.us/lasar2/) , which included data for 27 sampling locations and spanned the 
time interval of 1960-2005. 

There was a gap in the data for causal and response variables during the interval of 1984-1997. 
The majority of the nutrient data was in the form of dissolved inorganic nutrients rather than total 
nitrogen or phosphorous. Most of the data compiled was collected at fixed sampling locations, rather 
than through probabilistic sampling. All the data were collected along the main channel of the estuary, 
and did not extend into the sloughs. The locations of stations sampled extended trom the mouth to the 


fi¬ 


ll 



tidal fresh portion of the estuary. The estuary narrows upstream of about 25 km from the mouth and 
there was limited sampling upstream of this region. There were limited historic data for water clarity 
(secchi depth) and as a result we were unable to assess trends in water clarity. All of the chlorophyll a 
data compiled were obtained using spectrophotometric or fluorometric methods. 


Table 4.1 Sampling frequency for cruise data collected by U.S. EPA from 1998-2006. 



Sampling 

# of Sampling 

Distance from mouth 

Year 

Month 

Frequency 

Locations 

of Estuary (km) 

1998 

Jun, Jul, Sept, Nov 

Once a Month 

35 

2-21 

1999 

Jan-Dec 

Once a Month 

Varied 5-35 

2-21 

2000 

Jan-Dec 

Once a Month 

Varied 5-36 

2-21 

2001 

Mar, Apr, Aug-Oct 

Once a Month 

Varied 5-36 

2-21 

May-Jul 

Twice a Month 

Varied 12-34 

2-21 


Apr-Sept 

Weekly 

12 

2-21 

2002 

Jan, Feb, Mar, Oct 

Twice a Month 

12 

2-21 


Nov, Dec 

Once a Month 

12 

2-21 


Apr-Sept 

Weekly 

12 

2-26 

2003 

Jan, Feb, Dec 

Once a Month 

12 

2-26 


Mar, Oct 

Twice a Month 

12 

2-26 

2004 

April-September 

Twice a Month 

12 

3-26 

2006 

F ebruary-December 

Once a Month 

12 

3-35 


28 


















Table 4.2 Summary of historic data compiled include source of the data, temporal and spatial 
sampling frequency of the data set and parameters measured. 

Source 

Time 

Interval 

Number 

of 

Sampling 

Events 

# of 
Stations 

Dist. 

from 

mouth 

(km) 

Parameter 

Matson (1964) 

11/62-1/64 

30 

4 

2-81 

Salinity, Water temp, DO, PO 4 
, Si(OH ) 4 

Gibson and 

Snow (1967) 

6/66-11/67 

30 

6 

7-16 

Salinity, Water temp, DO 

De Ben et al. 
(1990) 

3/67-11/68 

42 

10 

2-26 

Salinity, Water temp, DO 

Gibson(1974) 

4/68 - 3/70 
8/71 - 1/72 

23 

11 

4 

2 

4-15 

15-16 

Salinity, Water temp, DO 

P0 4 3 ', Si(OH) 4 , N0 3 ’+ N0 2 " 

Johnson (1980) 

6/73 - 10/74 

80 

5 

9-16 

Chlorophyll a 

Amspoker 

(1977) 

12/73-8/74 

4 

6 

3-36 

NO 3 + NO 2 , Total Phosphates, 
Si(OH ) 4 

Karentz (1975) 

7/74 _ 4/75 

21 

4 

3-19 

Chlorophyll a 

Karentz and 
Mclntire (1977) 

5/74 - 5/75 

12 

4 

3-19 

Salinity, Water temp, NO 3 + 

N0 2 ‘, P0 4 3 ', Si(OH) 4 , 

Chlorophyll a 

WED 

unpublished 

7/76- 12/77 

9 

16 

3-42 

Salinity, Water temp, PO 4 , 

NH 3 , N0 2 ‘, N0 3 \ TN, TP, TSS 

Frey(1977) 

2/77 - 6/77 

8 

3 

3-35 

3- 

Salinity, Water temp, PO 4 , 

Si(OH) 4 , N0 3 '+ N0 2 \ 
Chlorophyll a 

Butler (1986) 

6/83 - 8/85 

12 

7-16 

2-35 

Salinity, Water temp, DO, NO 2 
, N0 3 ‘, NH 4 + , P0 4 3 ', Si(OH ) 4 

Arnold et al. 
(1992) 

4/86- 3/87 

149 

1 

11 

Salinity, Water temp, TSS 


29 
























4.2 Oregon Estuarine Classification Study 

As part of an effort to classify estuaries by the susceptibility of their submerged aquatic vegetation 
and food webs to nutrients, WED surveyed seven Oregon estuaries during the dry seasons of 2004 and 
2005 (Lee et ah, 2006). The estuaries sampled have regional drivers and landuse characteristics 
similar to the Yaquina Estuary. Their watersheds were primarily forested (66-86%) with low land 
development (high and low intensity development < 1%), and low human population densities (4 - 25 
individuals km" 2 ; Lee et al, 2006). The estuaries sampled (Alsea, Nestucca, Yaquina, Salmon River, 
Coos, Umpqua River and Tillamook) vary in size from 2 to 55 km , and from river dominated to ocean 
dominated. As is typical of many PNW estuaries, they have extensive intertidal zones with the 
percentage of intertidal area ranging from 32 to 87% of total estuarine area. 

Water quality data together with measurements of the natural abundance stable isotope ratio for 
nitrogen (5 I5 N) of green macroalgae data were collected to evaluate current water quality conditions. 
These data were also used to divide each estuary into oceanic and riverine dominated zones (in terms 
of nitrogen sources). The sampling consisted of high tide and low tide cruises and of short-term 
deployments of water quality datasondes. During each cruise between 10 and 17 stations were 
sampled in each estuary, depending upon the size of the estuary, and the stations extended from the 
mouth of the estuary to the fresh water portions of the estuary for all systems except Coos Estuary 
(lowest salinity in Coos was 14 psu). For more details on the methods used and the data collected, see 
Lee et al. (2006). 

4.3 National Coastal Assessment (NCA) 

As a part of the NCA, the Environmental Monitoring and Assessment Program (EMAP) 
assessed the condition of estuarine resources of Oregon based on a range of indicators of 
environmental quality, including water quality indicators (chlorophyll a , nutrients, and dissolved 
oxygen). The study utilized a stratified random sampling design and sampled over two years (1999- 
2000). The NCA Oregon estuary data set was obtained during the summer, and thus corresponds to 
the Yaquina Estuary “dry season.” The NCA data set allows comparison of Yaquina Estuary values 
for water quality parameters (e.g. median DO) to values for the same parameter across the set of all 
Oregon estuaries. 

Details of the sampling program and results of the Oregon NCA assessment are provided in 
Nelson et al. (2004). Briefly, the Oregon 1999 sampling design consisted of 50 sites distributed among 


30 


14 estuaries ot the State. An additional 30 sites were sampled in Tillamook Bay to assess condition of 
this National Estuary Program system, and Tillamook Bay was thus not included in the sample 
selection tor the other 50 sites. Tributary estuaries of the Columbia River that are located within 
Oregon were included in the 1999 sampling effort, while the main channel area was not sampled until 
2000. In 1999, estuaries were divided into four strata based on size, and approximately equal sampling 
effort was placed in each stratum, to insure sampling across the entire estuarine size spectrum. The 
Oregon 2000 study included only the main channel area of the Columbia River, and was split into two 
strata, the lower, saline portion and the upper, freshwater portion, with 20 and 30 sites sampled, 
respectively. Additional samples were obtained in the WA tributary estuaries of the Columbia River in 
1999, but were not included in the data presented in this section. A total of 128 out of the 130 target 
stations were successfully sampled for water quality indicators. 

4.4 Percentile Approach 

Previous assessments of water quality conditions in PNW estuaries were hindered by the 
limited availability of water quality data for estuaries in the region, particularly in Oregon (Bricker et 
al., 1999). Since there were limited data for applying the reference condition approach for the class of 
estuaries similar to the Yaquina Estuary, we used in situ observations within Yaquina Estuary as a 
basis for the Estuarine Reference Condition (as recommended by U.S. EPA, 2001). To accomplish 
this, we produced cumulative distribution functions (CDFs) for the Yaquina Estuary and compared 
those to CDFs of other Oregon estuaries using two independent data sets (Classification Study and 
NCA for Oregon estuaries). The NCA and Classification data sets were sampled at different temporal 
and spatial scales. The Classification data set used in our analyses included samples from six estuaries, 
with 10-17 stations sampled per estuary during both flood and ebb tidal conditions. Data from the 
Yaquina Estuary collected as part of the Classification Study were not included in the computation of 
percentiles for this data set. The NCA data set sampled 14 Oregon estuaries. The number of stations 
in each estuary was randomly determined within an estuarine size stratum. Timing of sample 
collection with respect to tidal stage was random. The number of sampling locations per estuarine 
system in the NCA data set ranged from 1 (Alsea and Yachats) to 67 (Columbia). 

In Appendix C, we present various classifications of the estuaries in the Classification Study 
and NCA data sets based upon geomorphology, susceptibility to nutrient pollution, and statistical 
clustering of physical and hydrologic variables. The number of classes ot estuaries (or types) depends 


n 


31 


upon the scale of the classification system as well as the classification system utilized (see Table C.l). 
The estuaries sampled in the Classification Study and NCA data set fell into a limited number of 
estuary classes (2-4); however, there was not a consistent pattern in the grouping of estuaries within a 
class among the different classifications. One exception was the Columbia River Estuary, which 
consistently was placed in a separate class for classifications based on geomorphology, susceptibility 
to nutrient pollution, and statistical clustering. 

Cumulative distribution functions (CDFs) were produced for each of the three data sets 
(Yaquina, Classification, and NCA). For this analysis, the data from the three data sets were divided 
into marine and riverine dominated regions (Zones 1 and 2, respectively). In addition, the Yaquina 
data set was further divided into wet and dry seasons. Only recent data (1998-2006) were used in 
creating the CDFs for Yaquina Estuary. The CDFs produced for the Yaquina and Classification data 
sets represent percentiles associated with the number of samples (i.e., not weighted by percentage of 
estuarine area). The NCA program typically computes CDFs using the appropriate sampling area 
weightings, which are based on areas of sampling strata determined from GIS (US EPA, 2004a). This 
allows estimation of the areal extent of Oregon’s estuaries associated with any value of an indicator 
variable. However, for the present study, estimates of percentiles for NCA data sorted by salinity zone 
were produced without use of area weightings and represent percentiles associated with the number of 
samples. This was done for consistency among data sets, and because area estimates of salinity zones 
were not available for all Oregon estuaries. An additional set of CDFs were produced for the NCA 
data set excluding the Columbia Estuary, which differs from the other Oregon estuaries in size, 
geomorphology, and other factors. 

4.5 Statistical Analysis 

Due to the non-normal distribution of the data, non-parametric statistical tests were used for all 
analyses. The Mann-Whitney Rank Sum test was used to determine whether there were significant 
differences in median values between zones or seasons. The Kruskal-Wallis one way analysis of 
variance on ranks was used to test whether there were significant differences in the median values 
between the Yaquina (dry season only), Classification Study, and NCA data sets. If there were 
significant differences (p<0.05), then Dunn’s test was used for pairwise multiple comparisons. For all 
tests, p values less than 0.05 were considered significant. 


32 


To assess whether there were temporal trends in water quality measures, the data were divided 
into zones and seasons to minimize biases associated with differences in sampling (spatial and 
temporal). For chlorophyll a , there was insufficient wet season data available, so trend analysis was 
only performed for the dry season. A Mann Kendall trend test was used to test whether there were 
significant trends within a zone and a season. If there were significant seasonal patterns within a zone, 
then the Seasonal Kendall test was used to determine if there was a significant increasing or decreasing 
trend. The Seasonal Kendall test performs the Mann Kendall test for each season and then combines 
the results of these into one overall test for whether there is a consistent monotonic trend over time 
(Helsel et al., 2006). For the Seasonal Kendall test all of the data (within a zone and season) was used. 
For the Mann Kendall trend test, there can only be one observation for each date, so multiple 
observations (either multiple stations or sampling events) on a single day were averaged. For all trend 
tests, p values less than 0.05 were considered significant. In addition to the trend analysis, we divided 
the data into historical and recent groups and tested whether there were significant differences in 
median values using the Mann-Whitney Rank Sum test. 

Mann-Whitney Rank Sum test and Kruskal-Wallis one way ANOVA were performed using 
SigmaStat software package (version 3.5, Systat Software, Inc., San Jose, CA), while trend analysis 
(Mann Kendall and Seasonal Kendall) were performed using a Windows Program written by the U.S. 
Geological Survey (Helsel et al., 2006). 


33 


1 ^ 


5. Spatial and temporal patterns in water quality parameters in the Yaquina Estuary 

For the analyses in this report, we divided the year into two seasons (wet and dry) and divided 
the estuary into two zones. There are significant seasonal and spatial patterns in the water quality data 
resulting from differences in sources, transport, and losses. 

5.1 Salinity 

There are strong seasonal differences in salinity within the Yaquina Estuary driven by 
differences in freshwater inflow (Figure 5.1 and Figure 2.3). During the dry season, Zone 1 is marine 
dominated with mean salinity of 26 psu at the boundary demarking Zones 1 and 2. Salt penetrates 
about 35 km into the estuary during periods of minimal freshwater inflow. 


Zone 1 Zone 2 



Figure 5.1 Salinity versus distance from mouth of the estuary during the a) dry and b) wet seasons. 
The gray lines demark the two zones 


34 




























5.2 Nutrients 


During the wet season, NO3 is the primary form of DIN in the estuary (median of 88% of DIN, 
n = 873). There is little utilization of dissolved inorganic nutrients by phytoplankton within the estuary 
during the wet season due to short residence time (high freshwater inflow) and low solar irradiance. 

The average incident photosynthetically active radiation (PAR) varies from 15 mol quanta m 2 d ’ 1 
during the wet season to 38 mol quanta m 2 d ’ 1 during the dry season. Mixing diagrams (property 
salinity plots) are often used to infer biogeochemical cycling occurring within estuaries (e.g., internal 
sources and sinks). Mixing diagrams of DIN for wet season cruises exhibit conservative mixing 
behavior, indicating river inputs are the primary nitrogen source and that there is little utilization 
within the estuary during this time. Minimal utilization of nutrients is also evident in the low 
chlorophyll a levels observed during the wet season (see Chapter 7). 

The dry season coincides with the growth season and with upwelling on the shelf. As 

discussed in Chapter 3, nutrient rich water associated with coastal upwelling is advected into Yaquina 

. 3- 

Estuary during flood tides. During the dry season, high levels of DIN and PO4 enter the estuary about 

two days after upwelling conditions (Brown and Ozretich, in review). Median concentrations of 

3- 

oceanic NO3 and PO4 entering the estuary during the dry season are 8.6 pM and 1.3 pM, respectively 

3- 

(n = 830). The maximal nutrient concentrations (NO3 =31.5 pM and PO4 = 2.9 pM) entering the 
Yaquina Estuary during upwelling periods are similar to those found in other upwelling regions 
(Dugdale, 1985) and elsewhere on the Oregon shelf (Corwith and Wheeler, 2002). 

During the dry season, NO 3 is the primary form of DIN (median of 75%, n =2028), while NO 2 
is a minor component only composing 2% of DIN. There is a mid-estuary minimum in mean dry 

season N0 3 ’+ N0 2 (with a mean of 7 pM, Figure 5.2) suggesting that the estuary receives N0 3 from 
both the ocean and the river. For mixing diagrams to be useful in identifying the importance of 
internal processes (e.g., biological uptake) steady state conditions need to apply. Due to the temporal 
variability of the ocean end member, it is not appropriate to use mixing diagrams to determine the role 
of internal estuarine processes (i.e., biological uptake) in the formation of this mid estuary minimum. 

3 - 3 - 

The primary source of PO 4 to the system is the ocean and there is a steady decline in P0 4 with 

distance into the estuary (Figure 5.3). The oceanic signal in N0 3 and P0 4 3 propagates approximately 
13 km up the estuary (Brown and Ozretich, in review). 


' t 


35 



Figure 5.2 Spatial variation in dry season NO3 + NCF . The line indicates a 2 nd order polynomial fit to 
the data (NO3 + NO2 = 20.4 - 1.9 * distance + 0.079 * distance 2 , r 2 = 0.1 5 , p < 0.001) 



0 5 10 15 20 25 30 

Distance from Mouth of Estuary, km 


Figure 5.3 Spatial variation in dry season P 0 4 . The line indicates linear regression (PC)/ = 1.55 - 
0.041 * distance, r 2 = 0.24, p < 0.0001). 


36 





















5.3 Chlorophyll a 


Previous studies have demonstrated that chlorophyll a is advected into estuaries along the 
Oregon and Washington coasts from the coastal ocean during the dry season (Roegner and Shanks, 
2001; Roegner et ah, 2002). Brown and Ozretich (in review) found similar results for Yaquina 
Estuary. In Yaquina Estuary, peak chlorophyll a concentrations imported from the coastal ocean 
during the dry season reach 50 pg E 1 with a median value of 4 pg l' 1 (n=181). The input of 
phytoplankton to the estuary lags upwelling favorable winds by approximately 6 days, suggesting that 
it takes this amount of time for phytoplankton to utilize the recently upwelled nitrogen and be 
transported across the shelf into the estuary (Brown and Ozretich, in review). 

Figure 5.4 shows the import of chlorophyll a from the ocean, as indicated by the fact that high 

chlorophyll a occurs at high salinities. The oceanic signal attenuates more rapidly for chlorophyll a 

3 - 

compared to NO3 and PO4 . The statistically significant relationship between oceanic chlorophyll a 
concentrations and within estuary chlorophyll a is only evident up to about 11 km into the estuary 
(Brown and Ozretich, in review). The more rapid decline in the ocean signal in chlorophyll a is 
probably the result of benthic grazing on oceanic phytoplankton. Oyster aquaculture is present in 
Yaquina Estuary in the region 10-15 km from the mouth (Figure 2.1) and in the lower estuary there are 
tidal flats that have high densities of filter-feeding burrowing shrimp (DeWitt et al., 2004, see Section 
3.2.3). Data from an in situ fluorometer (located 3.7 km from the mouth of the estuary) indicate that 
there is an import of oceanic chlorophyll a to the estuary and that a 60% reduction in chlorophyll a 
occurs between successive flood and ebb tides. Flood tide chlorophyll a values (median = 14 pg 1"') 

1 • 

were significantly higher than ebb tide values (median = 9 pg f ; Mann Whitney Rank Sum, p<0.001, 
n = 53). The import of chlorophyll a to Zone 1 is consistent with the findings of Karentz and Mclntire 
(1977) that during the spring through fall seasons marine diatom genera dominated in the lower estuary 
(stations 3.4 and 6.7 km from the mouth of the estuary), while freshwater and brackish taxa dominated 
in the upper estuary (stations located 12.3 and 18.8 km from the mouth). Phytoplankton blooms occur 
in the tidal fresh portion of the estuary as indicated by the high chlorophyll a values at low salinities 
(Figure 5.4). 


37 


1 1- 


80 

> 



Salinity, psu 


Figure 5.4 Dry season chlorophyll a versus salinity (all stations from 1974-2006) showing high 

chlorophyll a at high salinities, demonstrating the oceanic import of chlorophyll a from the 
coastal ocean into the Yaquina Estuary. Plot also shows the high chlorophyll a in the tidal 
fresh portion of the estuary. 

5 .4 Nutrient Limitation and Primary Productivity 

Potential for nutrient limitation of phytoplankton is often estimated by examining the ratio of 
dissolved inorganic nutrients relative to the Redfield ratio (16 mol N: 1 mol P) and comparing the 
ambient dissolved inorganic nutrient concentrations to phytoplankton half saturation constants for 
nutrient uptake (e.g., Eyre, 2000). Typically, if the N:P ratio of the water column falls below 10:1 then 
phytoplankton may be limited by nitrogen, and if the ratio is greater than 20:1 there is the potential for 
phosphorous limitation (Boynton et al., 1982). In addition, if the ambient water column concentrations 
are less than the half saturation constants for nutrient uptake then we assume that the phytoplankton 
may be nutrient limited. Typical half saturation constants for DIN and DIP are 1.0 - 2.0 pM and 0.1 - 
0.5 pM, respectively. 

The median N:P ratio during the dry season is approximately 12:1, suggesting that nitrogen will 
be depleted prior to phosphorous for the majority of the estuary. There is evidence of phosphorous 
limitation in the upper portions of the estuary (17- 27 km from mouth) with the N:P ratio reaching as 
high as 260:1. In only 12% of the estuarine sampling events was the N:P ratio greater than 20 and DIP 


38 







less than 0.5 pM, suggesting the potential for phosphorous limitation. During the dry season, the 
median DIN concentration is 14 pM (n=2028), and 95% of the time the DIN concentration is greater 
than 2 pM (typical half saturation constant for phytoplankton). In only 5% of the estuarine sampling 
events was the N:P ratio less than 10 and DIN less than 2 pM. This suggests that although the N:P 
ratio often falls below 16:1, the estuary is not usually limited by either nitrogen or phosphorous. This 
is supported by assimilation ratio data (primary production : chlorophyll a) of Johnson (1980) that was 
collected during the dry season at a station about 16 km from the mouth of the estuary (Figure 2.1). 
Johnson’s data showed 77% of the time there were sufficient nutrients for planktonic primary 
production, while 15% of the time there was borderline nutrient deficiency and 8% of the time there 
was evidence of nutrient depletion. 

Specht (1975) conducted algal bioassays at six locations in Yaquina Estuary during 1972-1975 
to examine the potential for nitrogen and phosphorous limitation. These experiments suggested that 
the upper portion of the estuary (26 km from mouth to tidal fresh) was predominantly phosphorous 
limited, while in the lower estuary, the system is nitrogen limited during the dry season and 
phosphorous limited during the wet season. 

There is limited water column primary productivity data for Yaquina Estuary. Water column 
primary production (at a station 14 km from the mouth of the estuary) during the dry season ranged 
from 0.25-2.8 g C m' 2 d' 1 with mean of 0.9 g C m' 2 d' 1 (Johnson, 1980). For comparison, primary 
productivity associated with benthic microalgae in the lower portion of the estuary (Zone 1) ranged 
from 125-325 g C m' 2 y' 1 (depending upon the location and elevation; Riznyk and Phinney, 1972). 
Davis (1981) measured net primary production during the dry season in the lower portion of the 
estuary of 46 g C m' 2 d' 1 and 0.26 g C m' 2 d' 1 for green macroalgae and benthic microalgae, 
respectively. Net primary production for Zostera marina and Z. japonica in the lower portion of the 
estuary was 181 and 130 g C m' 2 y' 1 , respectively (Kaldy, 2006ab). 

Based on the existing primary productivity data, Yaquina Estuary can be characterized as 
mesotrophic. Water column planktonic primary production is a minor component of the total primary 
productivity, which is dominated by benthic primary producers (macroalgae, microalgae and 
seagrasses). This is consistent with the findings of Valiela et al. (2000b) that for systems with 
moderate and high nitrogen loading, macroalgae is the dominant primary producer in short residence 
time estuaries (<3 days), while phytoplankton dominate in systems with relatively long residence times 

(> 45 days). 


' f 


39 


5.5 Relationships between TN and TP and Chlorophyll a 

Relationships between causal and response variables are useful for demonstrating the 
relationship between nutrient loading and biological effects. Several studies have found relationships 
between nutrients (nitrogen and phosphorous) and chlorophyll a in estuaries (e.g., Monbet, 1992; 
Smith, 2006; Dettmann and Kurtz, 2006). During 2006, we conducted monthly cruises of the Yaquina 
Estuary to examine if similar relationships were present. The cruises included 12 sampling stations 
extending from the mouth of the estuary to the tidal fresh region. During the dry season, the Yaquina 
Estuary receives nitrogen from both the riverine and oceanic sources, resulting in a curvilinear 
relationship in total nitrogen (TN) versus distance, while the ocean is the main source of phosphorous 
(TP) to the estuary (Figure 5.5). There is also a curvilinear pattern in the chlorophyll a versus distance 
resulting from oceanic input of chlorophyll a from the ocean (Figure 5.6). Relationships between dry 
season nutrients (TN and TP) and chlorophyll a are driven by ocean input (rather than a response to 
watershed nutrient sources) as evident by the significant trends of increasing chlorophyll a with 
increasing nutrients (TN and TP) in Zone 1 but not in Zone 2 (Figure 5.7 and Figure 5.8). Based on 
these findings, we feel that these types of relationships would not be useful for developing nutrient 
criteria for the Yaquina Estuary. 


40 



Distance from Mouth of Estuary (x), km 

Figure 5.5 Mean 2006 dry season a) total nitrogen (TN) and b) total phosphorous (TP) versus distance 
from mouth of estuary with error bars representing standard errors (n=12). Solid and dashed 
lines represent a 3 rd order polynomial and linear fit to TN and TP data, respectively. 



Figure 5.6 Mean dry season (2006) chlorophyll a versus distance from mouth of estuary with error 
bars representing standard error (n = 12) and line representing 2 nd order polynomial fit to the data. 


> t 


41 











Figure 5.7 Total nitrogen (TN) versus chlorophyll a for the dry season (2006) with data divided by 
zones and solid line showing significant regression for Zone 1. There is not a significant 
relationship for Zone 2. 



Figure 5.8 Total phosphorous (TP) versus chlorophyll a during the dry season (2006) with data 
divided by zones and solid line showing significant regression for Zone 1. There is not a 
significant relationship for Zone 2. 


42 








6. Nitrogen and Phosphorus as Water Quality Criteria 

6.1 Seasonal, Zonal, and Long-term Trends in N and P 

There are seasonal differences in water column nutrients within the estuary. DIN levels are 
significantly higher during the wet season (median = 21.1 pM, n = 874) than during the dry season (dry 
season median =13.9 pM, n = 2028; calculated using data from 1998-2006 combining Zones 1 and 2; 

3 - 

Mann-Whitney Rank Sum Test, p< 0.05). In contrast, PO4 levels during the dry season (median = 
0.97 pM, n = 2029) are almost twice as high as those during the wet season (median = 0.52 pM, n = 
873; calculated using data from 1998-2006 combining Zones 1 and 2; Mann-Whitney Rank Sum Test, 

p<0.05). During both the dry and wet seasons, Zone 1 has significantly higher PO4 concentrations 
than Zone 2, reflecting the ocean input of phosphorous (Mann-Whitney Rank Sum Test, p<0.05). The 

3 - . 3 - 

ocean input of PO 4 dominates during the dry season with Zone 1 PO 4 levels (median = 1.25 pM, n = 
1114) twice that of those in Zone 2 (median = 0.64 pM, n = 915). During the dry season, there are no 
significant differences in DIN levels between Zones 1 and 2 (Mann-Whitney Rank Sum Test, p>0.05). 
In contrast, during the wet season, DIN levels in Zone 2 (median = 55.2 pM, n = 354) are significantly 
higher than those in Zone 1 (median = 11.6 pM, n = 520), reflecting the dominance of riverine inputs 
(Mann-Whitney Rank Sum Test, p< 0.05). 

■F 

Because there are limited historic NH4 data, we were unable to assess whether there are any 
long-term trends in DIN; however, we do have sufficient historical data to examine trends in NO 3 + 
NO 2 (Figures 6 .1-6.4), the major component of DIN, and PO4 (Figures 6 .5-6. 8 ). Recent (1998-2006) 
dry season NO 3 + NO 2 and PO 4 3 in Zone 1 are significantly higher than historical data (Table 6.1 and 
Table 6.2; Mann-Whitney Rank Sum, p<0.001). In contrast, historical dry season N0 3 + N0 2 and 

3. 

PO 4 in Zone 2 are significantly higher than recent data (Table 6 .1 and Table 6.2). Peak wet season 
N0 3 + N0 2 concentrations in Yaquina Estuary are similar to wet season N0 3 ' observed in Oregon 

Coast Range streams (peak N0 3 of 172 pM; Wigington et al., 1998). The relatively high N0 3 + N0 2 
concentrations that occur in the historic data from Zone 1 dry season (Figure 6.1) are related to an 
anomalous freshwater inflow event in June 1984 (peak flow of 634 cfs compared to long-term mean 
for June of 81 cfs; calculated using data from Chitwood gauge on Yaquina River). Historical wet 

season N0 3 + N0 2 levels are significantly higher than recent observations in Zones 1 and 2 (Table 6.1; 


' /■ 


43 


Mann-Whitney Rank Sum, p<0.001). Wet season PO 4 in Zone 1 was significantly higher in the 
historical data set compared to recent, while in Zone 2 there was no difference between recent and 

historic PO 4 levels (Table 6.2; Mann-Whitney Rank Sum, p>0.05). 

There were no significant trends in NO3 + NCf within Zones 1 or 2 during either season 
(Figures 6.1 and 6.2; determined using the Seasonal Kendall and Mann Kendall tests). In Zone 1, there 

3 - 

was a significant increasing trend in PO4 during the dry season (Figure 6.3a) and a significant 

decreasing trend in PO 4 during the wet season (Figure 6.4a), while in Zone 2 there were not 
significant trends during either the wet or dry season (Figures 6.3b and 6.4b). Due to the opposing 

seasonal trends in Zone 1, there was not a significant trend in PO4 using the Seasonal Kendall test. 
Caution needs to be used in interpreting the trends and the differences in historic and recent 

3 - . 

median NO 3 + NO 2 and PO 4 levels (Tables 6 .1 and 6.2) due to differences in sampling frequencies. 
There are considerably more recent data (more stations and higher sampling frequency) than historic 

data, particularly in Zone 1 during the dry season (Table 6.1 and Table 6.2). Nutrient (NO 3 + NO 2 and 

3 - 

PO4 ) inputs associated with oceanic sources are highly variable depending upon the wind forcing and 
respond rapidly to changes in wind forcing. In addition, there is considerable interannual variability in 
oceanic input to estuaries (Brown and Ozretich, in review) associated with variability in upwelling 
(Corwith and Wheeler, 2002; Wheeler et al., 2003). During the dry season, the recent nutrient data 

3 - 

(NO3 + NO2 and PO4 ) are consistently higher than the historic data in Zone 1 (Tables 6 .1 and 6.2), 
possibly reflecting either differences in ocean conditions or better characterization of ocean input due 

to increased sampling frequency in recent data. In contrast, the historic nutrient (NO3 + NO2 and 

3 - 

PO4 ) median levels are higher than recent data in Zone 2 (Tables 6.1 and 6.2). Since the pattern in 
Zone 2 is opposite to that in Zone 1, this suggests that differences in ocean input are not driving this 
difference in Zone 2. Caution is needed in interpreting these differences since the trend analysis 

3 - 

revealed that there were no significant trends in nutrients (NO3 + NO2 and PO4 ) in Zone 2. The 
differences in water column nutrients during the last 30-40 years, although some are statistically 
significant, do not indicate a major change in nutrient loading (as inferred by nutrient concentrations) 
as experienced in other estuarine and coastal systems (e.g., Cloem 2001; Soetaert et al., 2006). 


44 



Table 6.1 Comparison of historic and recent NO3 + NO2 (pM) concentrations in the Yaquina 
Estuary. There are statistically significant differences in median concentrations between 
historic and recent data for all zones and both seasons (Mann-Whitney Rank Sum, n<0.001). 


Historic 

Recent 


Median 

Time Interval 
(Sample Size) 

Median 

Time Interval 
(Sample Size) 

Zone 1 





Dry 

6.5 

1974-1984 (157) 

10.0 

1998-2006 (1127) 

Wet 

19.8 

1974-1984 (65) 

8.5 

1998-2004 (520) 

Zone 2 





Dry 

14.0 

1971-1984 (247) 

9.8 

1998-2004 (919) 

Wet 

69.6 

1971-1984(148) 

52.6 

1998-2004 (354) 


Table 6.2 Comparison of historic and recent PO 4 (pM) concentrations in the Yaquina 

Estuary. There are statistically significant differences in median concentrations between 
historic and recent data for all zones and both seasons with the exception of Zone 2 wet season 
(Mann-Whitney Rank Sum, p<0.05) 



Historic 


Recent 


Median 

Time Interval 
(Sample Size) 

Median 

Time Interval 
(Sample Size) 

Zone 1 





Dry 

1.01 

1963-1.984 (223) 

1.25 

1998-2006(1126) 

Wet 

0.83 

1962- 1984(129) 

0.59 

1998-2004 (519) 

Zone 2 





Dry 

0.74 

1963-1984 (308) 

0.65 

1998-2004 (919) 

Wet 

0.54 

1962-1984 (212) 

0.49 

1998-2004 (354) 


45 


> r 






















Figure 6.1 Comparison of historic and recent NO 3 + NO 2 during the dry season in a) Zone 1 and b) 
Zone 2. 



Figure 6.2 Comparison of historic and recent N0 3 + N0 2 during the wet season in a) Zone 1 and b) 
Zone 2. 


46 








3.0- 


cf 1-5 

Q_ 


0.0 

3.0 


o 

Dl 1.5 


0.0 


a) 


PO=-16.2+ 0.0087* Year 

4 

p = 0.01 • 

• t = 0.13 




* 4 

1M 


b) 



i ; iy !> 



I960 


1970 


1980 1990 

Year 

3- 


2000 


Figure 6.3 Comparison of historic and recent PO 4 during the dry season in a) Zone 1 and b) Zone 2. 
The line in the upper panel shows a significant increasing trend in Zone 1 (Mann Kendall, p = 
0 . 01 ). 



The line in the upper panel shows a significant decreasing trend in Zone 1 (Mann Kendall, p< 

0 . 01 ). 


1 ^ 


47 











6.2 Percentile Approach for Nitrogen and Phosphorous 

There were significant differences in median DIN and PO4 values between the Yaquina (dry 
season), Classification Study, and NCA datasets for both Zones 1 and 2 (Kruskal-Wallis one way 
ANOVA on ranks, p<0.001). The dry season DIN concentrations observed for Yaquina Estuary 
(Zones 1 and 2) were significantly higher than those observed in the NCA data set both with and 
without the Columbia included (Table 6.3, Dunn’s method for pairwise comparison, p<0.05); however, 
there was not a significant difference between dry season DIN levels in the Yaquina Estuary (Zones 1 

and 2) and those observed in the Classification data set (Table 6.3). The PO 4 levels were 
significantly higher in the Yaquina Estuary (Zones 1 and 2) than those observed in the Classification 
and NCA data sets (Table 6.4, Dunn’s method for pairwise comparisons, p<0.05). The higher DIN and 

PO 4 3 levels in Zone 1 in the Yaquina Estuary as compared to the NCA data set is probably an artifact 
of sampling (both time of sampling and differences in the number of samples). In Zone 1, water 
column nutrients are dependent upon ocean conditions at the time of sampling. Inspection of the 
sampling dates during the 1999 NCA field effort, reveals that 82% of the estuaries were sampled 
during a time period of low nutrient conditions in the coastal ocean (determined using flood tide water 
temperature at Yaquina Estuary and a relationship generated between flood tide water temperature and 

NO3 + NO2 , for details see Lee et al., 2006). 

Zone 2 dry season DIN levels in the Yaquina Estuary are comparable to values for streams 

measured in Level III Ecoregion No. 1 - Coast Range (summer median NO3 + NO2 = 12 pM; U.S. 

3- 

EPA, 2000). The PO4 levels in Yaquina Estuary, particularly during the dry season and in Zone 1, 

are higher than Level III Ecoregion No. 1 - Coast Range values for streams (median = 0.28- 0.60 pM; 

3- 

U.S. EPA, 2000), due to the input of PO 4 from oceanic sources. Wet season DIN concentrations in 
Zone 2 of Yaquina Estuary are similar to wet season NO3 observed in Oregon Coast Range streams 
(median NO3 = 56 pM; Wigington et al., 1998) and streams data for Level III Ecoregion No. 1 - Coast 

Range (winter median NO 3 + N0 2 of 37 pM; U.S. EPA, 2000). Higher P0 4 3 levels in Zone 1 
compared to Zone 2 are present in the NCA and classification data sets, demonstrating that oceanic 

3 - 3 - 

input of P0 4 occurs at a regional scale. The DIN and P0 4 levels in Oregon estuaries would be 
considered to be medium levels using criteria from Bricker et al. (2003); however, based on analysis of 

sources (see Section 3.2) we believe that the high DIN and P0 4 3 levels are associated with natural 


48 


sources (i.e., red alder in the watershed and oceanic input) rather than anthropogenic sources. Systems 
in the PNW appear to have relatively high background levels of DIN and DIP compared to other 


estuaries in the U.S. (U.S. EPA, 2004a). 


Table 6.3 Percentiles for DIN (pM) calculated using Yaquina (1998-2006), Classification 
(2004-2005), and NCA Oregon estuaries (1999-2000) data sets. NCA and Classification 
values are for dry season only, while Yaquina data include values for dry and wet seasons. 


Percentiles for DIN (pM) 


Data set 

25 lh 

50 th 

75 th 

Sample Size 

Zone 1 

Yaquina 

Dry 

8.3 

14.1 

20.4 

1113 

Wet 

6.8 

11.6 

19.1 

520 

Classification 

5.7 

11.1 

18.6 

68 

NCA 

6.0 

8.6 

11.8 

36 

NCA excluding Columbia 

5.8 

8.4 

11.8 

33 

Zone 2 

Yaquina 

Dry 

7.3 

13.7 

23.1 

915 

Wet 

30.7 

55.2 

73.5 

354 

Classification 

8.3 

14.0 

36.9 

88 

NCA 

7.0 

9.4 

13.2 

89 

NCA excluding Columbia 

4.8 

7.2 

11.9 

27 


Table 6.4 Percentiles for PO 4 (pM) using Yaquina (1998-2006), Classification (2004-2005), 
and NCA Oregon estuaries (1999-2000) data sets. NCA and Classification values are for dry 

season only, while Yaquina 

include dry and wet season values. 




3- 

Percentiles for PCC fuM) 

• 

Data set 




Sample Size 

25th 

50 lh 

75 lh 



Zone 1 

Yaquina 





Dry 

0.88 

1.25 

1.69 

1114 

Wet 

0.39 

0.59 

0.77 

519 

Classification 

0.62 

0.89 

1.20 

68 

NCA 

0.76 

0.95 

1.15 

36 

NCA excluding Columbia 

0.76 

1.00 

1.16 

33 

Zone 2 

Yaquina 





Dry 

0.43 

0.64 

0.99 

915 

Wet 

0.41 

0.49 

0.62 

354 

Classification 

0.33 

0.45 

0.75 

88 

NCA 

0.35 

0.52 

0.71 

89 

NCA excluding Columbia 

0.18 

0.33 

0.73 

27 


i r 


49 




















7. 


Chlorophyll a as a Water Quality Response Measure 


7.1 Seasonal, Zonal, and Long-Term Trends in Chlorophyll a 

Chlorophyll a is often used as a surrogate for phytoplankton biomass and as an indicator of 
trophic status in eutrophication assessments (Bricker et al., 1999). There were seasonal differences in 
water column chlorophyll a in the estuary. Peak chlorophyll a levels occurred during the months of 
June to August (Figure 7.1). Seasonal chlorophyll a patterns were likely related to light limitation and 
flushing (as discussed in Section 5.2). The median wet season chlorophyll a was 1.6 pg f 1 (n = 293), 
while during the dry season the median increased to 4.9 pg F 1 (n = 1205). Dry season chlorophyll a 
levels in Zone 2 (Median = 5.3 pg F 1 , n = 229) were significantly higher than those in Zone 1 (Median 
= 3.3 pg F 1 , n = 347; Mann Whitney Rank Sum, p<0.001). During the late spring, there have been 
recurrent non-toxic red tide blooms of Myrionecta rubra in the vicinity of Toledo. During the dry 
season, chlorophyll a concentrations occasionally reached 15 pg F 1 in the vicinity of Toledo (8% of the 
recent observations). In the tidal fresh portion of the estuary, there were recurrent algal blooms during 
June and July, with chlorophyll a concentrations reaching 80 pg F 1 . 

There are limited historical data to assess long-term trends in chlorophyll a. Comparison of 
historic (1973-1983) and recent (2000-2006) chlorophyll a levels during the dry season reveal that 
there has been a decline in median chlorophyll a levels in both zones (Figure 7.2); although these 
declines are statistically significant (Mann Whitney Rank Sum, p< 0.001) they do not indicate a shift in 
trophic status of the estuary. There was a statistically significant decreasing trend in dry season 
chlorophyll a in Zone 1 (Mann Kendall, p<0.001), while in Zone 2 there was no significant trend. The 
changes in chlorophyll a that occurred in the Yaquina Estuary are small in magnitude (1 pg F 1 ) 
compared to changes that have occurred in other estuaries (Cloem, 2001; Harding and Perry, 1997). 

For example, in Chesapeake Bay chlorophyll a levels increased 5- to 10-fold in the lower portion of 
the estuary during the interval of 1950-1994 (Harding and Perry, 1997). A statistically significant 
zonal difference in chlorophyll a levels (Zone 2 higher than Zone 1) is present in the historic data as 
well as the recent data (Mann Whitney Rank Sum, p < 0.001). Peak chlorophyll a levels in Zone 1 
appear to be higher in the recent data compared to the historic; however, this is probably an artifact of 
sampling frequency. Blooms imported into Zone 1 from the coastal ocean are episodic in nature, 
reflecting the variability in wind forcing. Peak chlorophyll a levels in Zone 2 are similar for the 
historic and recent data. 


50 




Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 


Month 


Figure 7.1. Box plot of monthly chlorophyll a data from the Yaquina Estuary (all stations from 1973- 
2006). The dashed line indicates the Oregon estuarine chlorophyll a criterion. The boxes 
represent the 25 th and 75 th percentiles, the whiskers represent the 5 th and 95 th percentiles, and 
the horizontal line is the median. 


O) 


cc 


> 

_c 

CL 

o 

i— 

o 


O 



a) Zone 1 


25- 


o 



Median = 4. 
n = 137 
-1- 


6 


T 



I 


Median = 3.3 
n= 347 


50- 


25- 


b) Zone 2 



1-r 

1/1/1973 1/1/1978 1/1/1983 


Median = 6.1 

n = 377 
-1-r 




9 

• • 





ft 


1/1/1988 1/1/1993 1/1/1998 1/1/2003 


Median = 5.3 
n = 229 


Date 


Figure 7.2 Comparison of historic and recent dry season chlorophyll a for a) Zone 1 and b) Zone 2 in 
the Yaquina Estuary. The boxes indicate the time interval for the historic and recent median 
calculations. 


i ?■ 


51 








































































7.2 Percentile Approach for Chlorophyll a 

The chlorophyll a levels in Oregon estuaries, including Yaquina Estuary, are relatively low 
with median values of 2 - 5 pg l" 1 (Table 7.1). These chlorophyll a levels would be in the ‘low’ 
category when used as an indicator of eutrophication (Bricker et al., 1999) and in the ‘good’ category 
using the West Coast criteria for water quality parameters from the National Coastal Condition Report 
(US EPA, 2004a). The Oregon chlorophyll a criterion of 15 pg f 1 is exceeded 4% of the time during 
the dry season in Zones 1 and 2. At Elk City (tidal fresh part of the estuary) the 15 pg f 1 criterion is 
exceeded 28% of the time during the dry season (WED unpublished data; collected during 2002 and 
2003). There was a significant difference in median chlorophyll a between the Yaquina (dry season), 
Classification Study, and NCA datasets for both Zones 1 and 2 (Kruskal-Wallis one way ANOVA on 
ranks, p<0.001; Table 7.1). Dry season chlorophyll a levels in both zones of the Yaquina Estuary are 
significantly higher than those found in the other Oregon estuaries sampled in the NCA and 
Classification datasets (Dunn’s method for pairwise comparison, p<0.05). Although chlorophyll a 
levels in the Yaquina Estuary are significantly higher than for the other Oregon estuaries sampled, they 
are Tow’ compared to many other U.S. estuaries (US EPA, 2004a). 


Table 7.1 Percentiles for chlorophyll a (pg l’ 1 ) calculated using Yaquina (1998-2006), 
Classification (2004-2005), and NCA Oregon estuaries (1999-2000) data sets. NCA and 
Classification values are for dry season only, while Yaquina include dry and wet season 
values. 

Data set 

Percentiles for Chlorophyll a (pg l" 1 ) 

Sample Size 

25 th 

50 th 

75 ,h 

Zone 1 

Yaquina 

Dry 

2.2 

3.3 

5.7 

347 

Wet 

0.6 

u 

1.7 

95 

Classification 

1.0 

2.0 

3.8 

68 

NCA 

1.5 

2.1 

3.8 

36 

NCA excluding Columbia 

1.5 

2.0 

3.2 

33 

Zone 2 

Yaquina 

Dry 

3.8 

5.3 

7.9 

229 

Wet 

0.4 

0.9 

2.5 

46 

Classification 

0.8 

1.6 

2.5 

78 

NCA 

2.0 

3.3 

4.9 

89 

NCA excluding Columbia 

1.4 

1.8 

2.6 

27 


52 











8. Dissolved Oxygen as a Water Quality Response Measure 

Dissolved oxygen (DO) is an important water quality metric because of its effects on the well¬ 
being of estuarine resident and transitory organisms. Salmon and trout are particularly esteemed fishes 
in the PNW and traverse the estuaries during upstream and downstream migrations. The dissolved 
oxygen criterion for Oregon’s estuaries and streams focuses on the oxygen concentration needed for 
these fish because of their socioeconomic importance and their requirement for comparatively high 
oxygen levels. As a result, “salmon and trout rearing and migration” is a common designated use for 
Oregon coastal estuaries, including the Yaquina Estuary and River. 

Two species of salmon that are of particular importance in the Yaquina are the coho salmon 
(Onchorhynchus kisutch ) and steelhead trout ( O. mykiss). The reduced size of Oregon coastal coho 
populations have been a cause of particular concern. Chinook salmon ( O. tshawytscha) spawn and 
occur as juveniles in brackish waters, and are also present in the Yaquina River. The cutthroat trout 
( O. clarki ) is a fourth important salmonid species found in the Yaquina system, and a portion of this 
population also follows the salmon life history of migrating to the sea, where it grows to adulthood 
before returning to the natal stream to spawn. The timing of salmonid migrations through the estuaries 
varies by species, and is influenced by local conditions and hydrology. However, adults generally 
enter the estuary in the fall and progress upstream to freshwater spawning streams. The juvenile 
outmigrants, termed “smolts,” typically move downstream during the months of March to June. 

8.1 Seasonal, Zonal and Long-term Trends in Dissolved Oxygen 

There are strong seasonal patterns in dissolved oxygen within the Yaquina Estuary (Figure 8.1). 
Oxygen levels (expressed as both mg l" 1 and % saturation) in the estuary are comparatively stable 
during the wet season, but show a decline during the dry season. The wet season dissolved oxygen 
have an overall mean value of 9.7 mg f 1 (n = 869) dissolved oxygen. The dry season data were fitted 
using a nonlinear least squares procedure to have a descending cosine curve that begins at the wet 
season value of 9.7 mg l' 1 , declines to a value of 5.8 mg f 1 on August 2, and then returns to the wet 
season value. Zones 1 and 2 appear to follow the same pattern. Subsequent data analyses used the 
deviations from this modeled seasonal pattern (the solid line in Figure 8.1), so that the overall seasonal 
changes in oxygen concentration and differences in sampling would not confound more detailed 
analyses. All dissolved oxygen values used in the analyses were collected during daylight hours. 
Therefore, diel cycling of oxygen values due to plant photosynthesis and respiration are not 


< i- 


53 


represented in the data. Nighttime respiration can significantly reduce water column oxygen levels 
below daytime levels. 



Date 

Figure 8.1 Seasonal pattern in dissolved oxygen at all locations and all years in the Yaquina Estuary 
and River with squares and triangles representing samples from Zones 1 and 2, respectively. 
Solid line is nonlinear least-squares fit to data, which was modeled as a constant during wet 
season and a cosine function of date during the dry season. 

During the interval of 1960-1984, there was a significant trend of increasing DO in Zone 2 
during both the dry and wet seasons (Figure 8.2b; Mann Kendall, p<0.05). In addition, there was a 
significant seasonal trend in Zone 2 (Seasonal Kendall, p<0.05). Similar significant trends were found 
regardless of whether dissolved oxygen was expressed as non-transformed, residual, or percent 
saturation. A report by the Federal Water Pollution Control Administration (1966) stated that the 
water quality in the lower portion of the Yaquina basin was “adversely affected by existing and man¬ 
made conditions,” including “inadequately treated wastes from municipalities and industries” that 
placed “an excessive demand on oxygen resources of Yaquina Bay during annual periods of low 
streamflow.” In 1956, the City of Toledo upgraded their wastewater treatment facility to primary 


54 












Dissolved Oxygen residual, mg I 


treatment (prior to this raw sewage was discharged into the estuary), and in 1981 it was upgraded to 
secondary treatment. 



-'- 1 - 1 - 1 - 1 — in - 1 - 1 - 1 — 

1960 1970 1980 2000 2005 


Year 

Figure 8.2 Interannual trend in residual dissolved oxygen values during 1960 to 1986 for a) Zone 1 

and b) Zone 2. Zone 2 regressions are significant at the p< 0.05 level, while Zone 1 regressions 
are not. The solid and dashed lines represent the significant dry and wet season trends, 
respectively (Mann Kendall, p<0.05). Data from recent years are also shown for comparison, 
but were not included in regression computations. 


55 


< <■ 







There was also a decline of log rafting in the Yaquina Estuary from 1962 through the 1980s 
(Seddell and Duval, 1985). One effect on the water column of bark debris associated with log rafts is 
increased biochemical oxygen demand (Seddell and Duval, 1985). Due to the multiple stressors on the 
Yaquina Estuary during this time period, there is no way to determine the cause of the observed trend 
in DO levels in Zone 2. Recent (2002-2006) DO levels in Zone 2 are similar to DO levels during the 
mid 1980's, suggesting that there has been no recent changes in DO levels. In contrast, there were no 
significant trends in dry or wet season DO in Zone 1 (Figure 8.2a), suggesting that the trend in historic 
DO levels in Zone 2 was not a result of differences in ocean conditions. 

Since 2002 there has been an increase in the incidence of hypoxic events on the Oregon shelf 
(Grantham et al., 2004), which have the potential to influence DO levels within the estuary 
(particularly Zone 1). DO data collected 3.7 km from mouth of the estuary (using a YSI datasonde 
deployed at a mean depth of 1 m below the surface; WED, unpublished data) demonstrate that there is 
import of hypoxic shelf water into Yaquina Estuary during flood tides. A time series of DO and 
salinity measured during July 9-19, 2002, coinciding with a documented hypoxic event on the Oregon 
shelf off of Newport, Oregon (Grantham et al., 2004), clearly shows import of hypoxic shelf water to 
the estuary (Figure 8.3a). Minimum DO levels occurred during maximum salinities, demonstrating 
that the hypoxic water was imported into the estuary during flood tides. In addition, minimum DO 
levels occur during minimum water temperatures (~ 9 deg C), which is indicative of recently upwelled 
water. This trend of increasing DO with increasing temperature is opposite the trends of solubility, 
suggesting that differences in solubility are not causing the observed variability in DO levels. During 
this 10-day interval, minimum DO levels were 0.42 mg T 1 . The intervals of low DO conditions were 
relatively short, with DO levels increasing to 6-8 mg T 1 during ebb tides. The DO versus salinity plot 
(Figure 8.3b) shows that low DO levels occurred at high salinities (> 33 psu). A plot of dissolved 
oxygen expressed as percentage of saturation versus salinity had a similar pattern to that presented in 
Figure 8.3b, demonstrating that differences in solubility of dissolved oxygen are not the cause of the 
variability. 

The import of hypoxic shelf water into Oregon estuaries is not a recent phenomena. Gibson 
(1974) found low dissolved oxygen (5 mg T 1 ) in the lower Yaquina Estuary during July 1968, which he 
attributed to coastal upwelling. Callaway observed the intrusion of low dissolved oxygen (< 2 mg T 1 ) 
into the Umpqua Estuary (as cited in Percy et al., 1974). The NCA data set is also suggestive of 
import of low dissolved oxygen at a regional scale as indicated by lower DO values in Zone 1 


56 


compared to Zone 2. In a review of dissolved oxygen conditions in Oregon estuaries (ODEQ, 1995), 
the opposite spatial pattern was found, with minimum DO levels occurring near the upper end of salt 
water intrusion and higher concentrations associated with inflow of ocean water. They also stated that 
greater frequency of low DO would be expected if sampling occurred near the upper extent of salt 
water intrusion. Our results demonstrate that this may not be the case. 




Figure 8.3 a) Time-series of dissolved oxygen and salinity and b) salinity versus dissolved oxygen 
showing import of hypoxic ocean water at a station 3.7 km from mouth of estuary. 


t t 


57 


Salinity, psu 



























8.2 Percentile Approach for Dissolved Oxygen 

Dry season DO levels in the Yaquina Estuary are comparable to those found in other Oregon 
estuaries (Table 8.1) and are relatively high compared to other estuaries in the U.S. (U.S. EPA, 2004a). 
There was not a significant difference in dissolved oxygen levels in Zone 1 of the Yaquina Estuary 
(dry season using discrete samples) and Zone 1 of the other estuaries sampled in the Classification and 
NCA data sets (Kruskal-Wallis one way ANOVA on ranks, p> 0.05). The dissolved oxygen levels in 
Zone 2 of the Yaquina Estuary (dry season using discrete samples) are significantly lower than those in 
Zone 2 of the estuaries sampled in the Classification and NCA (including Columbia) datasets (Dunn's 
Method for pairwise comparison, p< 0.05). In the Yaquina Estuary during the dry season, the DO 
levels do not meet the Oregon criterion of 6.5 mg f 1 for 25% and 19% of the time in Zones 1 and 2, 
respectively (using discrete samples). There was not a statistically significant difference in dry season 
median DO levels between Zones 1 and 2 (calculated using recent discrete data; Mann-Whitney Rank 
Sum, p>0.05). During the wet season, DO conditions do not appear to be a cause for concern. 

There is considerable temporal variability in DO levels, which is not well captured in discrete 
point measurements. Continuous data are valuable in that they provide insight into the processes 
influencing observations such as the import of hypoxic water (Section 8.1), and they can allow 
evaluation of DO levels during both day and night conditions. Continuous data were available from 
datasondes deployed at two locations (Zone 1-3.7 km from the estuary mouth, Zone 2-18 km from 
the estuary mouth) in the estuary. The data were inspected to ensure that biofouling was not 
influencing observations, and only data from the first 7 days of each deployment was included in the 
analysis. In Table 8.1 we present the percentiles of the discrete and continuous data for the dry season 
for comparison; however, we did not perform formal statistical analyses due to the large difference in 
sample size. Median DO levels are lower for the continuous data compared to the discrete data, 
particularly in Zone 1 

Using the continuous data, we examined how often the State of Oregon DO criterion was not 
met during May-October of 2006. Dissolved oxygen levels fell below the 6.5 mg f 1 criterion 37% and 
28% of the time in Zones 1 and 2, respectively. The frequencies that observations fall below the 
criterion in the two zones are comparable but slightly higher than those calculated from the discrete 
samples (Table 8.1). A plot of salinity versus DO for the 2006 datasonde data has a pattern of low DO 
at high salinities (similar to that presented in Figure 8.3b) for the station in Zone 1; however, this 
pattern is absent in the datasonde data from the Zone 2 station. Based on these patterns, the below 


58 


criterion observations in Zone 1 are probably related to the oceanic import of hypoxic water, but 
possibly not in Zone 2. In the continuous data, there is not significant difference in dry season DO 
levels (median =7.0 mg 1 ') between the two locations (Mann Whitney Rank Sum, p>0.05). However, 
median DO levels expressed as percentage of saturation are significantly lower in Zone 1 than in Zone 
2 (Mann Whitney Rank Sum, p<0.001). The ODEQ (1995) review of the state DO criterion notes that 
in some bays, the 6.5 mg f 1 criterion may not always be achievable due to natural background 
conditions. This conclusion is consistent with the analyses in this section. 


Table 8.1 Percentiles for dissolved oxygen (mg l' 1 ) calculated Yaquina (1998-2006), 
Classification (2004-2005), and NCA Oregon Estuaries (1999-2000) data sets. NCA and 

Classification values are for dry season only, while Yaquina include dry and wet 

season 

values. 






Percentiles for Dissolved Oxygen (mg \ ) 


Data set 




Sample Size 

25 th 

50 lh 

75 lh 


Zone 1 

Yaquina 





Dry 





Discrete 

6.5 

7.8 

8.5 

46 

Continuous 

5.9 

7.0 

8.0 

2856 

Wet 

9.0 

9.4 

9.5 

36 

Classification 

6.7 

7.3 

8.3 

37 

NCA 

7.2 

7.8 

8.4 

35 

NCA excluding Columbia 

7.3 

8.0 

8.5 

32 

Zone 2 

Yaquina 





Dry 





Discrete 

6.7 

7.2 

7.6 

259 

Continuous 

6.4 

6.9 

7.5 

2862 

Wet 

10.2 

11.0 

11.6 

184 

Classification 

6.9 

8.1 

9.3 

53 

NCA 

7.6 

8.5 

9.0 

88 

NCA excluding Columbia 

6.7 

7.3 

8.3 

26 


11 - 


59 











9. Water Clarity {kj) and Turbidity as Water Quality Response Measures 


9.1 Seasonal and Zonal Patterns in Water Clarity and Turbidity 

A turbidity maximum occurs about 15 km from the mouth of the Yaquina Estuary (Figure 9.1). 
The water is relatively clear throughout the year in the lower estuary due to the input of ocean water, 
which is evident in the low turbidities and light attenuations near the mouth (0-5 km; Figures 9.1 and 
9.2). Turbidity tends to decrease upriver of the turbidity maximum (Figure 9.1). There is a significant 
increase in light attenuation with distance from the mouth of the estuary during both the wet and dry 
seasons (Figure 9.2). In situ light attenuation measured at fixed stations up estuary from the turbidity 
maximum at 16 and 19 km from the mouth was generally greater than for fixed sites at 4 and 9 km 
from the mouth (Figure 9.3). This difference is not as clearly seen in the turbidity data set (Figure 9.1). 

There was not a significant difference in dry and wet season total suspended solids (TSS) with 
median values of 7.8 and 8.9 mg f 1 , respectively (Zones 1 and 2 combined; Mann Whitney Rank Sum, 
p> 0.05). TSS levels in Zone 2 (Median =11.7 mg F, n = 119) were significantly higher than those in 
Zone 1 (Median = 6.5 mg f 1 , n = 158; Mann Whitney Rank Sum, p<0.001). Light attenuation is 
positively correlated with turbidity (r = 0.70, n = 1400), but not with chlorophyll a. 



Figure 9.1. Spatial variation in turbidity during wet and dry seasons (1998-2006). 


60 








Zone 1 Zone 2 

Ocean Dominated River Dominated 



Figure 9.2 Light attenuation coefficients (kj) versus distance from the mouth of the estuary from 
cruise data (years 1998 to 2006), with filled and open symbols representing dry and wet 
seasons, respectively. Solid line - dry season regression ( kd= 0.34 + [0.069 * Distance], r 2 = 
0.33, p<0.001). Dashed line - wet season regression (kd= 0.36 + [0.057 * Distance], r 2 = 0.30, 
p<0.001). While dry and wet season light attenuation are significantly correlated with distance, 
their regression coefficients are not different. Combining both seasonal data sets yields (kj= 
0.34 + [0.066 * Distance], r 2 = 0.32, pO.001). 


61 























2.4 


2.1 - 

1 . 8 - 

1.5- 

t e 12 “ 

"-o 

0.9- 

0 . 6 - 

0.3- 

0 . 0 -- 
0 

Figure 9.3. Median monthly light attenuation coefficients from the continuous data set at 5 locations in 
Yaquina Estuary (1999-2003). 

9.2 Percentile Approach for Water Clarity and TSS 

Due to methodological differences (NCA data set) and missing data (Classification data set), 
we calculated CDFs for light attenuation for only the Yaquina Estuary data sets. During the wet 
season, there is not a significant difference between light attenuation coefficients (kj) computed from 
the continuous and cruise data within each zone (Table 9.1; Mann Whitney Rank Sum, p>0.05). 

During the dry season, the light attenuation coefficients computed using the continuous data are 
significantly higher than those computed from the cruise data (Mann Whitney Rank Sum, p<0.001), 
and this difference is greater in Zone 2 (median about 16% higher for continuous) than in Zone 1 
(median about 8% higher). Median light attenuation coefficients within Zone 1 during the dry season 
were significantly higher (5-8% for continuous and cruise data, respectively) than those from the wet 
season (Mann Whitney Rank Sum, p<0.05). Within Zone 2, median light attenuation coefficients were 
significantly higher (10-27% for cruise and continuous data, respectively) in the dry season than in the 
wet season (Mann Whitney Rank Sum, p<0.001). 



i- 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 -r 

1 2 3 4 5 6 7 8 9 10 11 12 

Month 


62 






For the Yaquina Estuary, median values of TSS were similar within Zones between wet and dry 
seasons, but Zone 1 median values were approximately half that of Zone 2 values (Table 9.2). This 
pattern is consistent with the presence of a turbidity maximum at 14 km up the estuary, within Zone 2. 
There was a significant difference in median TSS between the Yaquina (dry season), Classification 
Study, and NCA datasets for both Zones 1 and 2 (Kruskal-Wallis one way ANOVA on ranks, p<0.001; 
Table 9.2). Comparison of the Yaquina Estuary data to that from both the Classification and NCA data 
sets showed some differences in zonal patterns. In Zone 1, there was not a significant difference 
between median TSS levels between the Classification and Yaquina data sets, but the Zone 2 median 
value in the Classification data set was significantly lower (66%) than the value for the Yaquina 
(Dunn’s method for pairwise comparison, p<0.05). NCA data for TSS showed still a different pattern 
across the region, where the median value was significantly higher (23%) than for the Yaquina in Zone 
1, while the median value was significantly lower (50%) than that for the Yaquina in Zone 2 (Table 
9.2; Dunn’s method for pairwise comparison, p<0.05). This pattern from the NCA data was present 
regardless of whether samples from the Columbia River Estuary were included. 

The NCA study used a probability based sampling within the dry season that was generally 
random with respect to tidal stage. Much of the sampling from the Yaquina Estuary for TSS was from 
cruises during flooding tides. It is not clear whether the zonal pattern differences observed were the 
result of methodology differences or that the Yaquina Estuary is somehow different in its spatial 
pattern for TSS. 


63 


< f 


Table 9.1 Percentiles for light attenuation coefficient kj (m 1 ) calculated using continuous 
(1999-2003) and cruise (1998-2006) data sets from the Yaquina Estuary for dry and wet 

seasons. 






Percentiles for Light Attenuation Coefficient kj (nf') 


Data set 




Sample Size 

25 lh 

50 th 

75 lh 


Zone 1 

Yaquina 





Dry 





Continuous 

0.62 

0.78 

1.00 

678 

Cruise 

Wet 

0.56 

0.72 

0.94 

541 

Continuous 

0.55 

0.74 

0.89 

505 

Cruise 

0.53 

0.66 

0.87 

248 

Zone 2 

Yaquina 





Dry 





Continuous 

1.14 

1.53 

2.26 

376 

Cruise 

Wet 

1.09 

1.32 

1.72 

439 

Continuous 

0.97 

1.20 

1.54 

247 

Cruise 

0.95 

1.20 

1.54 

178 


Table 9.2 Percentiles for TSS (mg f') calculated using Yaquina (1998-2004), Classification 
(2004-2005), and NCA (1999-2000) data sets. NCA and Classification values are for dry 
season only, while both dry and wet season values are provided for the Yaquina Estuary. 

Data set 

Percentiles for TSS (mg f') 

Sample Size 

25 th 

50 lh 

75 th 

Zone 1 

Yaquina 

Dry 

3.0 

6.3 

9.6 

102 

Wet 

3.7 

6.6 

12.2 

56 

Classification 

4.6 

6.7 

10.8 

66 

NCA 

10.4 

14.0 

16.0 

36 

NCA excluding Columbia 

11.0 

14.0 

16.1 

33 

Zone 2 

Yaquina 

Dry 

6.9 

11.2 

19.6 

83 

Wet 

6.6 

12.3 

34.7 

35 

Classification 

1.4 

3.8 

8.2 

83 

NCA 

4.0 

6.0 

10.4 

91 

NCA excluding Columbia 

5.0 

9.0 

11.8 

27 


64 




















10. Macroalgal Biomass as a Water Quality Response Measure 

10.1 Introduction 

Excessive algal growth is one of the major symptoms of eutrophication in coastal estuaries 
(Bricker et al., 1999). Three of the principal classes of algae are phytoplankton, epiphytic algae, and 
macroalgae. In PNW coastal estuaries, epiphytic algae (attached to other organisms) and macroalgae 
(seaweed) generally are considered to be of greater concern than is excessive growth of phytoplankton, 
which is rapidly transported out of the estuaries by tidal exchange. In this section, we report on the 
macroalgae issue as it relates to the question of eutrophication in Yaquina Estuary. 

10.2 Approach 

Beginning in 1997, numerous studies involving macroalgae have been conducted in Yaquina 
Estuary by WED. These include aerial photomapping surveys in 1997 and 1998, and intensive ground 
surveys of percent cover and biomass during 1998-2004. A listing of the individual studies conducted, 
and the analytical approaches utilized here, are presented in Appendix B. 

10.3 Results and Discussion 

10.3.1 Annual Variation: 1997 - 1998 

The aerial distributions of benthic green macroalgae documented in the aerial photography of 
July 23, 1997 and August 10, 1998 indicate a substantial increase in coverage in 1998 (Figure 10.1). 
Part of the increase very probably is due to the fact that the 1998 aerial photographs were taken two 
and one half weeks later than were those in 1997. However, based on seasonal percent cover 
distributions obtained in 1999-2000 (Figure 10.2), an increase in cover of only about 15% would be 
expected. In contrast, the benthic macroalgal cover of bare substrate on August 10, 1998 was 
approximately 250 % that on July 23, 1997 (Fig. 10.1). 

An empirical model has been developed that uses flood tide water temperatures to predict N0 3 

+ N0 2 concentrations in coastal ocean water entering Yaquina Estuary during flood tides (Brown and 
Ozretich, in review). The average concentrations predicted by this model for two-month intervals 
preceding the aerial surveys of 1997 and 1998 are 2.1 and 6.0 pM, respectively. This difference in 

average N0 3 + NO? concentration is assumed to be the result of the 1997 El Nino condition that 


' i- 


65 


suppressed normal upwelling of nutrient-rich subsurface water that year (Corwith and Wheeler, 2002). 
The ~ 300 % increase in the modeled nutrient concentration agrees well with the ~ 200 % net increase 
in intertidal macroalgal cover between 1997 and 1998 surveys. 

10.3.2 Seasonal Variation: 1999-2000 

Monthly averages (+ 1 std. error) for percent cover and biomass of benthic green macroalgae 
were measured for six sites (Appendix B) in Zone 1 during 1999-2000 (Figure 10.2). Maximum 
values occurred in September - October for both percent cover (~ 50 %) and biomass (~ 200 gdw m‘“), 
with rapid declines in November. Between December 1999 and May 2000 the respective averages 
were below 5 % and 5 gdw m‘“. In Zone 1 more than 95% of the intertidal cover and biomass 
accumulation for benthic green macroalgae occurred during the dry season. 

Macroalgal composition was assessed in 2001, and consisted of taxa most closely resembling 
Ulva lima : ~60%; U. fenestrata : ~30%; U. flexuosa : ~10%; U. intestinalis: <5%; (WED unpublished 
data). The seasonality and peak biomasses are consistent with historical data sets from the lower 
portion of Yaquina Estuary. Davis (1981) observed mean biomass of 400-500 gdw m' for green 
macroalgae during June to September, 1980 and Garber et al. (1992) observed green macroalgae 
biomass of 185-370 gdw m" during June to October of 1984 and 1985. These comparisons suggest 
that there was no increase in the frequency or intensity of macroalgal blooms within Yaquina Estuary 
over this 20 year period. 


66 






I I Eelgrass dominated 

1997 macroalgac 

1998 macroalgac 

1997 and 1998 macroalgae 







Figure 10.1 Photomap of intertidal vegetation in Yaquina Estuary from aerial surveys of July 23, 1997 
and August 10, 1998 illustrating interannual differences of benthic green macroalgae cover. 


> f 


67 










70 

60 

50 

40 

30 

20 

10 

0 


> 

< 

0 

Q) 

(Q 

0 

O) 

c 

a 

o 

of 

O 

o 

< 

0 



Figure 10.2 Average percent cover and biomass values (+ 1 std. err.) of benthic green macroalgae in 
the Yaquina Estuary between June 1999 and May 2000. The values are averages for six sites 
within Zone 1. 


10.4 Percentile Approach 

CDFs for both dry and wet season were calculated for benthic green macroalgal biomass data 
from 1998-2004 within Zone 1 of Yaquina Estuary. In addition, CDFs were calculated for data from 
the Classification surveys conducted during the dry season in Zones 1 and 2 of six other Oregon 
estuaries (Table 10.1). 


68 




Table 10.1 Percentiles for benthic green macroalgae biomass (gdw nf 2 ) for Yaquina Estuary 

(1998 - 2004, Zone 1 only) and the Classification data set (2004 - 2005). 


Data set 

Percentiles for Macroalgae Biomass 

Percentile for 

Sample Size 




25 lh 

50 th 

75 ,h 

100 gdw nf 2 

Zone 1 

Yaquina 






Dry 

0 

34.9 

189.8 

62.7 % 

4432 

Wet 

0 

0 

2.9 

95.1 % 

2142 

Classification 

0 

0 

11.6 

92.9 % 

351 

Zone 2 
Classification 

0 

0 

0 

99.6 

231 


Median Zone 1 dry season macroalgal biomass between 1999-2004 in Yaquina Estuary (Table 
10.1) was less than the mean value (83 gdw nf 2 ) measured from six band transects in 1999-2000 
(Figure 10.2), and considerably higher than the median value from the Classification study. In the 
Yaquina Estuary, biomass exceeded 100 gdw nf" for 20% of the intertidal area, compared with only 1- 
6% of intertidal area for the six estuaries of the Classification Study. The Classification Study found 
that >98% of benthic green macroalgae occurred in the ocean dominated Zone 1 of the Yaquina 
Estuary (Lee et al., 2006). The reasons for the higher algal biomass found in the Yaquina Estuary 
compared to other Oregon systems studied is not clear, and makes extrapolation of information to the 
rest of the Oregon coast difficult. 

10.5 Comparisons with Findings from Other Regions 

Literature review demonstrates that there is a wide range of macroalgal densities that cause, or 
are correlated with, negative effects on estuarine organisms (Appendix D, Table D.2). Water 
temperatures reported in the reviewed literature ranged from 9 to 20 °C versus 8-18 °C for Zone 1 of 
Yaquina Estuary, and were thus reasonably similar. Approximately one-third of the studies reported 
negative ecological effects from macroalgae for percent cover values of >50% and biomass densities of 
>200 gdw m' 2 . In the Yaquina Estuary, -27% of the intertidal zone exceeded 50% cover, and -10% 
had macroalgal biomass exceeding 200 gdw nf 2 (Lee et al., 2006). We also note that this density (200 
gdw m 2 ) is twice the threshold accepted for damage by macroalgae to seagrass in Chesapeake Bay 
(Bricker et al., 2003). 


i f 


69 












Literature values for macroalgal impacts suggest that during the dry season, the accumulation 
of benthic green macroalgae could have a negative effect on the abundance of some infaunal 
invertebrates (while possibly enhancing epifauna), and on certain other fauna (e.g., juvenile flatfish, 
shorebirds). However, the preponderance of green macroalgae occurs in the marine dominated Zone 1 
of Yaquina Estuary during the dry season. Results from stable isotope studies (Section 3.3 and Lee et 
al., 2006) provide strong support for the conclusion that benthic green macroalgae in Zone 1 of the 
Yaquina Estuary derive most of their nutrients from tidal influx of near shore marine waters. Summer 
green macroalgal blooms thus appear to be a natural response of the estuarine system. Thus, at 
present, the occurrence of benthic green macroalgae does not appear to be a useful indicator of 
eutrophication in Yaquina Estuary. 


70 


11. Submerged Aquatic Vegetation (SAV) as a Management Objective (Designated Use) 

11.1 Background 

The NHEERL Aquatic Stressors Framework (U.S. EPA, 2002) defines loss of submerged 
aquatic vegetation (SAV) as a major assessment endpoint for nutrient effects research. Seagrass, the 
dominant marine SAV, provides a critical three-dimensional structure often used by commercially and 
ecologically important species as a refuge from predation, and simulates estuarine biogeochemical 
cycling through trapping and recycling of seston and leaf material in sediments. Seagrasses also 
influence water quality and clarity by attenuating current velocity, promoting sediment deposition, and 
removing nutrients (N and P) from the water column. Thus, seagrass habitats function in a way that 
improves the quality of coastal and estuarine ecosystems. Sustaining seagrasses has become an 
important priority for federal agencies, the States, and tribes. 

Eelgrass ( Zoster a marina ), the principal seagrass in PNW estuaries (Phillips, 1984), is a rooted, 
flowering plant, which is present in many temperate estuaries world wide (den Hartog, 1970). Eelgrass 
meadows serve as a nursery ground for juveniles of commercially important species such as Pacific 
herring ( Clupea pallassi) and as a refuge for juvenile salmonids (Griffin, 1997; Simenstad and 
Wissmar, 1985; Levings, 1990; den Hartog, 1977). Eelgrass meadows are significant sites of primary 
production and eelgrass shoots can be utilized directly for food by some waterfowl such as the western 
black brant {Branta bernicula) (Griffin, 1997; Kentula and Mclntire, 1986), and indirectly by many 
species via consumption of detritus (Thayer et al., 1975). Eelgrass roots stabilize the sediment (Thayer 
et al., 1975) and the presence of eelgrass dampens wave energy which may serve to reduce erosion and 
to enhance larval settlement (Orth, 1992). Because of these characteristics, species abundances in 
eelgrass patches are usually greater than in other estuarine habitats (Everett et al., 1995). In 
recognition of the importance of seagrass beds, EPA Region III has proposed a “Shallow-water Bay 
Grass Designated Use” for Chesapeake Bay to insure adequate protection of living resources. 

Anthropogenic nutrient additions have been suggested by many authors as a cause for the 
dramatic decline in seagrasses world wide and for Z. marina in particular on Atlantic Coasts (Short et 
al., 1995: Valiela et al 2000a; Hauxwell et al., 2003). The principal effect of excess nutrients is to 
reduce light available at leaf surfaces via enhanced macroalgal and leaf epiphyte production and by 
increasing the water column light attenuation coefficient (kd) through the stimulation of the production 
of phytoplankton (Hauxwell et al, 2001; Madden and Kemp, 1996). 


11- 


71 


The Yaquina Estuary contains ~98.5 hectares of eelgrass which covers approximately 5% of 
the total area of the estuary (Figure 11.1). This eelgrass is in three zones consisting of: 1) a permanent 
bed of perennials in the lower intertidal and subtidal 1 (below Mean Lower Low Water, MLLW), 2) an 
intertidal transition zone (0.0 m to 0.5 m above MLLW) consisting of perennial patches and annual 
shoots; and 3) an upper intertidal zone (0.5 m to 1.5 m above MLLW) consisting of only annual shoots 
(Bayer, 1979). 



Newport 


Toledo 


Zone 2: 
Riverine 


Dominated 


Zostera marina 


Marine 

Dominated 




is 


Figure 11.1 Spatial distribution of Yaquina Estuary eelgrass. 


1 Below-1.0m MLLW 


72 































11.2 Spatial Seagrass Patterns 


The spatial distribution of Z. marina was determined within the Yaquina Estuary from 1997 to 
the present utilizing aerial photographs and false-color near-infrared (color infrared, CIR) film (Young 
et ah, 1999). Details on how this analysis was accomplished are presented in Lee et al. (2006) and 
Appendix B. 

Permanent bed perennial shoots make up the vast majority (90%) of the eelgrass population 
(Boese and Robbins, in prep.), and almost all of this eelgrass is in the intertidal zone in both the ocean 
(Figure 11.2) and river dominated (Figure 11.3) estuarine portions. Details on the methods used to 
generate Figures 11.2 and 11.3 are presented in Appendix B. The portions of the graphs corresponding 
to depths deeper than -1.5 m (MLLW) may have errors due to limitations in mapping methods and 
bathymetric modeling. Most (97%) of the Z. marina in the Yaquina Estuary is located in ocean 
dominated estuarine portions (Figure 11.1), which is illustrated by the differences in y-axis scales in 
Figure 11.2 and Figure 11.3. Although the distribution suggests an effect of salinity on eelgrass 
distribution, Z marina appears to be able to tolerate a wide range of salinities (Nelson, 2005). 

Z. marina also appears able to survive short-term exposures to fresh water, however, net leaf 
photosynthesis decreases in waters with salinities below 5 and totally ceases in completely fresh water 
(Hellblom and Bjork, 1999; Biebl and McRoy, 1971). Within the Yaquina Estuary, Kentula and 
DeWitt (2003) found that salinity appeared to be a statistically significant factor in controlling the 
within estuary distribution of Z. marina , even though the reported mean summer and winter salinity 
ranged from 25 to 33, which are well within published tolerance limits for Z. marina (Nelson, 2005). 
The results of the Kentula and DeWitt (2003) study were complicated by changes in light attenuation 
and temperature that tended to co-vary with salinity. Results may have been further complicated since 
the bathymetry of the Yaquina Estuary changes with distance from the estuary’s mouth such that the 
amount of suitable area in the optimal depth range for seagrass growth becomes limited in upriver 
estuarine segments (Lee et al., 2006). 


73 


i r- 


16 

14 

12 

10 
in 
CD 

I 8 

(D 

X 6 
4 
2 
0 

Figure 11.2 Z marina depth distribution in the marine dominated portion (Zone 1) of Yaquina 
Estuary. 


■Ma=axfTTTlTTTT~nT 


CD 

> 

CD 


CD 

CD 

CO 

c 

CD 

CD 


Thnn - frjrm - i -. 


cD^CNioc»cp^;CNiqcoco^CN|ooqcD^;CNOCN'^cpcqqcsj^;cooqocsi'>tcD 
CO CO CO CO C\i C\i CN C\i CN t— t —• t— t— t — O O O O O O O O O t— t — y— r— r— CN C\i CM CN 


Depth Below MLLW (m) 



co^ojqcqcq^cNqoqco^cNiqccco^CMOCM'^cocDOCN^cocDOCN'^cD 
COCOCOCOCNCNCNCNCN't— -t-t-t— t— OOOOOOOOOt— t— t— t— CsJCNCsicsi 


Depth Below MLLW (m) 


Figure 11.3 Z. marina depth distribution in the river dominated portion (Zone 2) of Yaquina Estuary. 


74 









































11.3 Temporal Seagrass Patterns 


Aerial photos of the Yaquina Estuary suggest that there is little year to year variability in Z. 
marina coverage from 1997 to the present. For example, Figure 11.4 shows details of the spatial 
distribution of a large Z marina meadow and a narrow fringing bed, both of which are in a portion of 
the ocean dominated area of the estuary. Although there are apparent differences in these seagrass 
coverages across years, most of these differences are likely within classification error limits resulting 
from differences in ambient lighting conditions, the presence/absence of small amounts macroalgae, 
and subtle differences in how photos were interpreted. For the fringing seagrass bed in Figure 11.4, 
there is a possible relationship between seagrass temporal variability and the formation of intertidal 
drainage channels. Fringing seagrass beds which grow on steeply sloped sites are often less 
aggregated and tend to form into elongated and complicated shapes (Fonseca et al., 1983; Fonseca and 
Kenworthy, 1987; Frederiksen et al., 2004) thus providing more bed edges where erosion may be more 
effective in dislodging shoots. Erosion and strong physical disturbance events have often been 
observed in these marginal seagrass areas of the Yaquina Estuary where tidal drainage channel changes 
and storm events have either eroded Z marina bed margins or deposited large woody debris on top of 
them (Boese and Robbins, in prep.). Episodic events such as these have been implicated in other 
studies (e.g. Krause-Jensen et al., 2003) as factors which alter shallow water seagrass populations. 
Thus, it is likely that the Z marina losses observed at this marginal seagrass habitat area of the 
Yaquina Estuary were due to natural rather than anthropogenic stressors. Overall the result of our 
aerial surveys, when coupled with ancillary published (Boese et al., 2003; Young et al., 1999) and 
WED unpublished data, indicate that over the past decade the spatial distribution of Z marina within 
the Yaquina Estuary has been stable. 

The oldest known spatial coverage data for Z marina in the Yaquina Estuary were published in 
the Oregon Estuary Plan Book (Cortright et al., 1987). This coverage was based on aerial photographs 
that were taken in the mid 1970’s. A comparison of this historical coverage (Figure 11.5) to the 
present Z marina distribution (Figure 11.1 and Figure 11.4) suggests an overall loss of seagrass in the 
Yaquina Estuary. However, there is no indication of what was meant by “seagrass bed” in terms of 
percent cover criteria that were used to delineate areas where Z marina was present or absent 
(Cortright et al., 1987). In general, where seagrass habitat is shown on the Oregon Plan Book map, 
some seagrass is found in that general location either in recent photographs or has been observed as 
less than 10% cover during recent ground truthing surveys. Considering the differences in 


i r 


75 


methodologies, there appears to be no gross differences in the spatial distributions of Z. marina within 
the Yaquina Estuary over the last thirty years. 



Figure 11.4 Comparison of the spatial distribution of Z. marina in a portion of the Yaquina Estuary 
(see inset on Figure 11.1) from 1997, 2000, and 2004. Figure shows a large contiguous 
meadow (north of channel) and a narrow fringing bed (south of channel). 


76 


















Newport 


C c\> 

0.0 - 0.5ra \fu3v 

IkX^" 




<0.0m MLLW 


■:'r' 


Zone 2: 


iW4f 


Riverine 


Dominated 


Zone I: 
Ma line 


Sea grass Bed 


Dominated 




x£ 


_ 




Figure 11.5 Historical distribution (mid 1970’s) of Z. marina from the Oregon Estuary Plan Book 

(Cortright et al., 1987). 

11.4 Water Clarity and Seagrass Lower Depth Limit 
11.4.1 Background 

The depth distribution of seagrasses has been shown to be dependent upon light penetration, 
with coastal seagrasses in general extending to depths receiving, on average, —11% of the irradiance at 
the water’s surface (Duarte, 1991). If the maximum depth that seagrasses grow in an estuary is a result 
of water clarity alone, then the maximum depth to which seagrass grows might be used as an 
integrative water quality assessment measure (Dennison et al., 1993), and has been suggested for use 
as a monitoring tool (Sewell et al., 2001; Vimstein et al., 2002). Additionally, understanding the 
minimal light requirements for seagrasses is necessary for preservation of existing seagrass meadows 
and for restoration purposes (Batiuk et al., 2000; Dennison et al., 1993; Fonseca et al., 1998). 

Light criteria have been proposed as part of the guidelines for restoring and maintaining Z. 
marina habitat in Chesapeake Bay (Batiuk et al., 2000). However, applying these values to the U.S. 
Pacific Coast is problematic due to differences in tidal amplitude that tend to narrow the depth range of 


' !■ 


77 

























seagrasses (Koch and Beer, 1996) and due to other differences including lower temperature ranges and 
faster estuarine flushing rates. Criteria for Chesapeake Bay Z marina were derived for a spring 
through fall growing season (Batiuk et al., 2000), when carbohydrates are accumulated and used to 
maintain plants during the winter when they cannot maintain a positive carbon balance (Zimmerman et 
al., 1989). In contrast, for Z. marina in the Yaquina Estuary, winter irradiance appears to be sufficient 
for the maintenance of a positive carbon balance and as a result plants continue to grow through the 
winter, albeit at a slower rate (Boese et al., 2005). 

11.4.2 Methods 

During the summers of 2004 and 2005 the lower depth limit of Z. marina was determined at 64 
randomly selected locations in Yaquina Estuary by underwater video and direct visual observation 
techniques (for methods see Appendix B). These data were then compared to calculated light 
attenuation coefficient (kj) values that were measured at or near the same locations during a series of 
sampling cruises (See Section 4.1.1). 

11.4.3 Relationship between Lower Margin and Water Clarity 

Figure 11.6 shows the relationship between the lower depth limits of Z. marina and the distance 
from the mouth of the Yaquina Estuary. Although this relationship shows a great deal of variability, 
the lower depth limit appears to be greater toward the estuary’s mouth and in the ocean dominated 
estuarine areas. There also appears to be a difference in this depth-distance relationship depending 
upon whether it is determined in the ocean or river dominated sections of the estuary, as illustrated by 
the lack of a significant linear relationship when the data from the ocean dominated section of the 
estuary are excluded (Figure 11.6). 

The reduction in the Z. marina lower depth limit is consistent with a reduction in mean water 
clarity, which was also linearly related to distance from the mouth (Figure 9.2). We derived an 
additional relationship between the lower limits for Z. marina and the estimated kd values as follows. 

Depth = 4.4 - (1.79*fo), r 2 = 0.32. (11.1) 

where Depth = m below Mean Sea Level (MSL) 2 and kd is the light attenuation coefficient (m 1 ) 
computed from Figure 9.2 (for wet and dry seasons combined) at the location of the lower limit 


2 For the Yaquina Estuary MSL = MLLW + 1.39 m 


78 



observation. 



Figure 11.6 Relationship between distance from mouth of the estuary and the lower depth limit (below 
Mean Sea Level) for Z. marina. The three regression lines are for Zone 1, Zone 2, and the 
entire estuary. 

The maximum depth to which a seagrass grows is dependant upon water clarity which is often 
presented in.the literature as the fraction of surface irradiance found at the maximum seagrass 
colonization depth (Duarte, 1991). This value is calculated from kd as 



where / is the irradiance at depth, I 0 is the surface irradiance, kd is light attenuation coefficient, and z is 
depth (m below MSL). Using this equation, the amount of surface irradiance reaching the observed 
lower depth limits for Z. marina within the Yaquina Estuary was estimated. These values ranged from 
7 to 68% with a mean ± standard deviation of 12.6 ± 1.9 % (n = 64). Standard deviation was 
determined using the propagation of error associated with estimating by linear regression from 
multiple kd values at a given distance from the mouth of the estuary (see ANOVA with regression in 
Sokal and Rohlf, 1981). 


t t 


79 



The trend for deeper depth limits with increased water clarity is evident for all species of 
seagrasses, where the maximum colonization depth corresponded to approximately 11% of surface 
irradiance (Duarte, 1991). However, the data for Z marina presented by Duarte (1991) suggested that 
the amount of light needed to sustain this species at depth is almost double that for seagrasses in 
general (Table 11.1). Duarte (1991) went on to note that the world-wide relationship between kj and 
the maximum seagrass colonization depth (all species) was linear, and that it could be simply 
calculated as: 



where Z c is the maximum colonization depth (m). 

Duarte (1991) also noted that this result was similar to the results obtained for Z marina (Z c = 
1.62/kj and Z c = 1.53 /kj) on the Atlantic Coast of the U.S. (Dennison, 1987) and within Danish 
estuaries (Nielsen et al., 1989), respectively. The trends in Z c and kj for Z marina in the Yaquina 
Estuary are consistent with Duarte’s (1991) relationship (Figure 11.7) even though the waters in the 
Yaquina Estuary were more turbid than those reported by Duarte (1991). These kj values from Duarte 
(1991) were converted (Equation 11.2) to the percent of surface irradiance at Z c to generate the values 
which are presented in Table 11.1. Also included in Table 11.1 are the minimum light requirements 
recommended for the growth and survival of SAV in Chesapeake Bay (Batiuk et al., 2000). 

Although the mean percent of surface irradiance needed to maintain Z marina in the Yaquina 
Estuary is lower than literature values, they are within the range of published values. Literature values 
were either determined in waters which were considerably less turbid than those of the Yaquina 
Estuary (Duarte, 1991) or derived from a synthesis of literature values and area specific research 
(Batiuk et al., 2000). The Z marina values published by Duarte (1991) rely heavily on Danish studies, 
especially Nielsen et al. (1989), which account for 20 of the 29 literature values shown in Figure 11.7. 


80 



10 


8 - 

3 

oo 

S 6 - 
£ 

<D A 

JD 4 - 

*5 2 - 

cl L 

<D 

Q 

0 - 


0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 

K d 

Figure 11.7 Relationship between Z marina maximum depth limit (m below MSL) and kj. Filled 
circles represent data from Duarte (1991) and hollow circles represent data from the Yaquina 
Estuary. 


• • 


O • Q 

0 °?D 

"°°° % 

- ®°o° 

• °°: 


• • 


o 

>° 

O o°6> 

O 


& 

O O 
8 


00 


o 

o 

8 


o 

e 


8 


Table 11.1 Comparison of mean and range of percent of water column surface irradiance needed 
to maintain Z. marina at its colonization depth from published data and from Yaquina Estuary 
data. SE = standard error. 




Range 

Source 

Mean 

N 

Max 

Min 

Duarte (1991) 

20.5 

29 

43.9 

4.7 

Current Study 

12.6 

64 

68.3 

7.2 

Batiuk et al. (2000) 

22 a 




a Value is not a mean but according to the authors is based on an analysis of literature and on an 

evaluation of monitoring and modeling research. 




81 


n 















11.5 Epiphyte Patterns and Impact on Z. marina 

The Chesapeake Bay water quality criteria for shallow water bay grass includes values for 
percent of ambient light reaching a plant through the water column, and a value for percent of light at 
the leaf, after attenuation by epiphytes. A study of epiphytes growing on Z. marina leaves was 
conducted within the Yaquina Estuary from 2000 though 2004 at six stations distributed between 3.5 
and 17 km upriver from the mouth of the Yaquina Estuary. Methodological details are presented in 
Appendix B. 

11.5.1 Spatial and Temporal Patterns in Epiphytes 

In the Yaquina Estuary, there was a general annual pattern in 2000 though 2003 in which 
epiphyte biomass increased in the spring to a maximum in the summer and fall. This statistically 
significant parabolic relationship was most clearly seen on the older, external seagrass blades within a 
shoot (Figure 11.8). For unknown reasons, this yearly pattern was not observed in the 2004 samples. 

In the Yaquina Estuary, epiphyte biomass per unit surface area of seagrass leaves was higher in 
Zone 1 (ocean dominated) than in Zone 2 (river dominated) in both wet and dry seasons (Figure 11.9). 
However, only the dry season differences were statistically significant. Epiphyte biomass per unit leaf 
surface area was higher in the dry season than the wet season within both zones (Lee et al., 2006). 


82 



Figure 11.8 Temporal relationship of epiphytic biomass per unit leaf area on Z. marina external leaves 
in the Yaquina Estuary, 2000-2003. 

12 -t- 


10 -\ 



Wet Season - External Dry Season - External Wet Season - Internal Dry Season - Internal 


Figure 11.9 Epiphyte biomass per unit leaf area on old (external) and young (internal) Z. marina 
leaves by season (wet or dry) and salinity zone in the Yaquina Estuary. 


83 


n 






























There was a significant positive linear relationship (Figure 11.10) between percent light 
reduction and log+1 transformed biomass data, for external and internal blades combined. The linear 
regression relationship overestimates light reduction for the low epiphyte biomass samples. 



Figure 11.10 Linear regression relationship between the percent of light reduction to log(x+l) 
transformed epiphyte biomass per unit Z. marina surface area. 

Epiphytes reduced the amount of light reaching the surface of Z. marina leaves. The monthly 
range of variation in light reduction was high, ranging from 4 - 91% for external leaves, and 2-62% 
for internal leaves. The range in mean light reduction for a plant was estimated as 3 - 76 %, with an 
overall mean estimated light reduction of 53% (n=18, SE=4.6). As a result of the spatial differences in 
epiphyte biomass within the two salinity zones in the estuary, average light reduction for a plant was 
higher in Zone 1 (61%, n=72, SE=3.4) than in Zone 2 (37%, n=44, SE=4.5) as a result of the more 
heavily fouled external blades. 

11.6 Zoster a marina Light Requirements 

Minimum light requirements for maintaining and restoring SAV have been proposed for 
Chesapeake Bay (Batiuk et al., 2000) and Puget Sound (Thom et ah, 1998). Chesapeake Bay light 
criteria values were empirically estimated by measuring the maximum depth of SAV annually and 


84 









associated kj values monthly (Dennison et al., 1993). For Chesapeake Bay, proposed water column 
light requirements vary by estuarine salinity classification with higher light requirements suggested for 
polyhaline and mesohaline zones (>22% of surface irradiance) than for tidal fresh and oligohaline 
zones (>13% of surface irradiance). These zonal differences are in part due to the different species of 
SAV which are typically found in the different salinity zones (Batiuk, 1992). It is also important to 
recognize that the Chesapeake Bay values are designed to be protective of multiple SAV species, and 
not just Z. marina. The zonal irradiance values were based upon previously published kj values of 2.0 
m' 1 for tidal fresh and oligohaline sections and 1.5 nf 1 for polyhaline and mesohaline sections (Batiuk 
et al., 1992). The proposed irradiance criteria were adjusted for the amount of light absorbed by 
epiphytes encrusting SAV leaf surfaces and reported as the Percent of Light at the Leaf surface or PPL. 
These minimum PPL values were 9 and 15 % respectively for the two salinity groupings (Batiuk et al. 
2000). These proposed criteria were also applicable only to the SAV growing season (typically spring 
though fall). 

In contrast, light requirements for Z marina in Puget Sound were reported as integrated light 
intensity levels (Thom et al., 1998). These were estimated using maximum seagrass depth measures, 
kj values and production-irradiance (P vs. I) relationships. Based on this methodology Thom et al., 
(1998) suggested that to maintain the greatest densities of Z. marina, ~300 pmoles m" s' (3 moles m‘~ 
d' 1 ) were required for at least three hours daily during the growing season. Thom et al. (1998) went on 
to suggest that for Z marina to minimally persist would require mid-day minimum irradiance values at 
the maximum depth limit to be approximately 150 pmoles nf" s' during the year. These same values 
are also suggested as minimum requirements for outer coast PNW estuaries like Willapa Bay and Coos 
Bay (R. Thom, Pacific Northwest Environmental Laboratory, pers. comm.). Assuming that mid-day 
surface irradiance is in the range of 1000-2000 pmoles m' 2 sec' 1 , the minimum light requirement 
corresponds to approximately 15-30% of surface irradiance, which is consistent with other published 
criteria values and with the present study (Table 11.1). Additional verification of these minimum light 
requirements within the Yaquina Estuary is currently in progress (WED unpublished data). The mean 
daily irradiance value was approximately 3.8 moles m' 2 d 1 at a single lower margin site for Z marina 

in the Yaquina Estuary (WED unpublished data). Although this value exceeds the Thom et al. (1998) 

- 2-1 

criteria, irradiance values were highly variable, ranging from 0.5 to 7 moles m d , with extended 
periods of apparently inadequate lighting at depth from October to December (WED unpublished 


11 


85 


data). However, even during these periods of apparently inadequate irradiance, Z. marina in the 
Yaquina Estuary continued to grow (Boese et al., 2005). 

While it is tempting to directly apply the existing light criteria values to PNW estuaries like the 
Yaquina, there are several additional factors that need to be considered. The estuaries from which 
Duarte (1991) and Thom et al., (1998) derived their relationships are generally less turbid (mean kj~ 
0.5 nf 1 ) than the Yaquina (see Table 9.1). Z. marina has been shown to adapt to lower winter 
irradiance by increasing chlorophyll content (Zimmerman et al., 1995). Although we are not aware of 
any study that documents an analogous response to turbidity, a similar response to chronically more 
turbid water might allow for deeper colonization. 

Temperature is a possible confounding factor. The range of near-surface temperatures within 
Chesapeake Bay, Puget Sound, and in the estuaries used in Duarte’s (1991) review are likely greater 
than those observed within the Yaquina Estuary (Boese et al., 2005) due to the latter’s twice daily 
flushing with cold ocean water. Increased respiration rates due to higher summer temperatures would 
potentially need to be offset by increased irradiance for plants not only to maintain themselves but to 
store carbohydrates in rhizomes which could then be used to maintain the plant during the winter when 
irradiances may be less than optimal (Zimmerman et al., 1995; Burke et al., 1996; Zimmerman and 
Alberte, 1996). Therefore, it is possible that eelgrass in the Yaquina Estuary may require less spring 
and summer irradiance to perform the same function because of the generally cooler waters of these 
systems. 

Additionally, the Yaquina Estuary is mesotidal. Koch and Beer (1996) found that greater tidal 
'amplitude reduced the range of water depths that Z. marina colonized in Long Island Sound. Due to 
increased tidal amplitudes and turbidity, Z. marina growing in western Long Island Sound was limited 
to a 1 m depth range compared to the 4 m range observed in eastern Long Island Sound (Koch and 
Beer, 1996). Plants that are forced into a narrower depth range by these factors are likely to be more 
vulnerable to stressors such as storm events which may have contributed to the historic losses of Z. 
marina meadows. Thus, to assure seagrass survival in mesotidal and macrotidal estuaries, it may be 
prudent to establish more restrictive water clarity requirements in those estuaries. 

Our study of epiphytes growing on Z. marina leaves in the Yaquina Estuary revealed a 
reduction in the amount of epiphyte biomass in upriver, lower salinity areas. With the exception of 
2004 there appeared to be a seasonal pattern in epiphyte biomass such that the greatest biomass 
occurred in the summer and fall, when ambient light levels are highest. The accumulation of epiphytes 


86 


was estimated to reduce the amount of light reaching leaf surfaces by an average of about 60% in the 
ocean dominated portion of the Yaquina Estuary (Zone 1). At present we are not sure how epiphyte 
load and its impact on light availability to eelgrass leaves compares to that found in other estuaries, but 
such variation will need to be considered in future efforts to derive water column light criteria for Z. 
marina. Epiphyte light reduction will be incorporated in future versions of the seagrass stress-response 
model described in Chapter 12. 

Although the effects of tides, temperature and epiphytes constitute current uncertainties in 
estimating minimum light requirements for seagrass in Yaquina Estuary, general conclusions can be 
made. Maximum depth of colonization of eelgrass in Yaquina Estuary suggests that a mean of 12.7% 
of surface illumination is required for persistence of seagrass at the deepest edge of the bed. Applying 
the median light extinction coefficient (kj) for Zone 1 (0.8 m' 1 , Chapter 9) to Equation 11.2 yields an 
estimate of percent of surface illumination at depths of 1, 2, and 3 m of 36, 20 and 9 %, respectively. 
This suggests that the use of the median kd as a criterion in Zone 1 would allow persistence of eelgrass 
to a depth between 2-3 m. Use of the median kj for Zone 2 (1.5 m' 1 ) in Equation 11.2, yields estimates 
of percent surface illumination at depths of 1 and 2 m of 22 and 5%, respectively. This suggests that 
the use of the median kj as a criterion in Zone 2 would allow persistence of eelgrass to a depth between 
1-2 m. These results are generally consistent both with empirical data on bathymetric distribution of 
eelgrass within the Yaquina Estuary and with the conclusions generated by use of the Stressor- 
Response Model (see Chapter 12). 


87 


11 - 


12. Stress-Response Approach for Protection of SAV 


12.1 Introduction 

A previous report summarizes EPA research to develop mechanistic modeling approaches for 
examining the sensitivity of seagrasses to nutrient stressors (Kaldy and Eldridge, 2006). Here we use 
the mechanistic Seagrass Stressor-Response Model (SRM) developed by Kaldy and Eldridge (2006) in 
a heuristic fashion to assess the protective capacity of the Percentile approach (see Section 4.4). 

The SRM used the 25 th , median, and 75 th percentile results from the cumulative distribution 
function (CDF’s) developed in Sections 6.2 and 9.2 to determine if these potential criteria are 
protective of seagrass distribution and biomass in Yaquina Estuary. Our approach was to use the 
quartile values from the CDF’s as inputs to the seagrass SRM with the objective of testing which 
values maintained seagrass at present depth distributions and which values resulted in decline of 
seagrass. These evaluations will provide guidance to aid in the selection of water clarity criteria that 
are protective of seagrass habitat in PNW estuaries. The response variables of the SRM model were 
seagrass biomass and carbohydrate content. 

The SRM is composed of a set of mechanistic models that can be run in a variety of 
configurations depending on the study or management goals. The advantage of this approach is that, • 
unlike the regression model approach, we can examine the direct and indirect effects of particular 
environmental conditions. Full model details and validation description are provided by Kaldy and 
Eldridge (2006). 

12.2 Description of Model 

The seagrass SRM was developed through an integrative effort that used a variety of data 
sources such as field studies and manipulative experiments, published literature, and existing and new 
models. A detailed description of the SRM development, calibration and validation is provided by 
Kaldy and Eldridge (2006). Briefly, the SRM is composed of an Allocation Model, a Plant 
Productivity Model and a Sediment Diagenetic Model. The Allocation Model integrates field data and 
provides estimates of carbon, nitrogen and phosphorus fluxes between plant components and the 
environment. These flux rates are then used to parameterize the Plant Productivity Model. The 
seagrass Plant Productivity model predicts above-ground biomass, carbohydrate reserves and plant 
growth in response to nutrients (both water-column and sediment porewater), salinity and underwater 


88 


light, and it provides boundary conditions for the Sediment Diagenetic Model. The Sediment 
Diagenetic Model provides estimates of the inorganic chemical environment in the root zone of the 
plant. The build-up or depletion of particular compounds in the sediments may have positive or 
negative effects on seagrass health and production. These models can be run independently or can be 
coupled together and run as the full SRM. Only the plant model configuration was used in the current 
analysis as there were no sediment geochemical data for the upper Yaquina Estuary (Zone 2). 
Calibration data for the plant model are shown in Appendix Figure E. 1. Field and mesocosm 
experiments were used to validate model predictions (Kaldy and Eldridge, 2006). 

The SRM has been used to examine seagrass response to a number of environmental variables 
including nutrients, canopy level irradiance, water turbidity, and organic matter input to sediments. 

The SRM can be used to assess the effectiveness of proposed nutrient loading criteria designed to be 
protective of seagrass. Assessment of the protective capacity of a particular water quality criterion was 
based on evaluation of trends in modeled seagrass biomass and carbohydrate for each depth interval. 

A downward trajectory in simulated biomass indicates that the water quality criterion was not 
protective at that depth. We also looked at the clustering of model outputs to assess breakpoints 
among the depth contours. Large differences in biomass or carbohydrate concentration between 
contours provides an approximation of the depth where conditions become inhospitable. 

Models are simplifications of observed processes; as such they are subject to a number of 
simplifying assumptions and caveats. Further these models are being revised to include new types of 
calibration data that presumably will produce more accurate predictions. For example, the SRM does 
• not include the effects of irradiance attenuation due to epiphytes, algae, self-shading or surface 
reflectance. As a result, the current simulations represent the “best case scenario” for underwater light. 
The model does include the effects of turbidity, nutrients, and salinity (Appendix Figure E.2). 
Furthermore, seagrass physiology was assumed to be similar between Zones 1 and 2. For presentation 
purposes, an upper margin for seagrass distribution of 0.2 m above mean lower low water (MLLW) 
was used; however, there are areas throughout the bay where the upper limit can not easily be defined 
by a single bathymetric level as a result of differential effects of desiccation, erosion, and sediment 
deposition. 


• i- 


89 


12.3 Model Simulations and Input Data 


The SRM was used in a heuristic fashion with idealized input data. Composite temperature and 
salinity time series were generated using YSI datasonde data from two stations, one located in each 
zone (distances from mouth of the estuary of 3.7 and 17.9 km). The composite time series represented 
average conditions from 1999-2003. For the solar irradiance, a composite incident photosynthetically 
active radiation (PAR) time series was generated using data collected by WED during 1999-2003 
(Appendix Figure E.2). This PAR time series represents average incident light conditions (I 0 ) in the 
study area at 15 minute intervals for the year. The underwater light environment used in the model 
includes daily variations in surface irradiance with the addition of tidal variations in water surface 
elevation, and zonal and seasonal differences in water clarity ( k ). The underwater light environment 
was simulated for each zone as 

/ ( z /=i,...,« > A 0 = 1 o (0 e ~ kjS (Zi +h) 

where j represents the zone, z, represents the depth (relative to MLLW), h is tidal variation in water 
surface elevation, and kjs is the diffuse light attenuation coefficient for the specific zone (j) and season 
( S ), which were obtained from the percentile analysis calculated using the continuous Yaquina Estuary 
data set (Table 9.1). Tidal variations in water surface elevation (/?) were incorporated using hourly 
water level data from a tide gauge in Zone 1 (http://tidesandcurrents.noaa.gov7. Station 9435380 South 
Beach). 


Table 12.1 Input data from percentile approach for different SRM simulations. . 


Case 

Zone 

Dry Season 

Wet Season 

kd , nf 1 

DIN, pM 

kd , nf 1 

DIN, pM 

1 

(Median) 

1 

0.78 

14.1 

0.74 

11.6 

2 

1.53 

13.7 

1.20 

55.0 

2 

(25 th %) 

1 

0.62 

8.3 

0.55 

6.8 

2 

1.14 

7.3 

0.97 

30.7 

3 

(75 th %) 

1 

1.00 

20.4 

0.89 

19.1 

2 

2.26 

23.1 

1.54 

73.5 


Simulations were run for 3 cases, the median, 25 th , and the 75 th percentiles as representative of 
different levels of potential protective criteria for Yaquina Estuary. For these analyses, the estuary was 
divided spatially into a lower (Zone 1) and upper (Zone 2) region and temporally into a wet and dry 
season (Table 12.1). The transition from wet to dry season conditions for both kj and DIN 


90 


















concentrations was done as a step-function (Table 12.1). In these model simulations, water column 
DIN was used in nutrient uptake kinetics for the seagrass plant, but does not include indirect nutrient 
effects such as epiphyte, macroalgal or phytoplankton blooms. Additionally, simulations were 
conducted for parameters at a series of depths ranging from 0 m relative to mean lower low water 
(MLLW) down to a maximum depth of 5 m below MLLW in Zone 1 and 0 to 2.5 m below MLLW in 
Zone 2. The expanded depth range in Zone 1 was used because of lower light attenuation in this 
region. Additionally, this range encompasses the known depth distribution of Z. marina in Yaquina 
Estuary and allows for the expansion of the seagrass into deeper waters. 

12.4 Results 

As described above, the individual model simulation runs differed by depth, irradiance 
attenuation and DIN (Table 12.1), and temperature and salinity (Appendix Figure E.2). Temperature 
and salinity were also different between Zones 1 and 2, with a greater range in each variable occurring 
in Zone 2 as a result of seasonal heating and cooling. The model incorporated functions that increased 
photosynthesis and metabolism with temperature. The larger range in water temperature in Zone 2 
affected seagrass physiology, while the relatively stable water temperatures in Zone 1 had a minimal 
impact (Appendix Figure E.2). Salinity had no influence on seagrass biomass or production in Zone 1 
but affected production in Zone 2 during winter months. 

Model results indicated that the median values would maintain the existing distribution of 
seagrass within Yaquina Estuary (Figure 12.1). Current maps indicate that seagrass covers 
approximately 0.97 km' 2 in Zone 1 and 0.013 km' 2 in Zone 2. In Zone 1 (lower estuary), seagrass 
would be protected to a depth of about 2 m below MLLW. The median values are representative of 
present conditions. In Zone 2 (upper estuary), the median criteria would protect seagrass to a depth of 
about 0.5 m below MLLW. Model simulations indicated that criteria based on the 25 th percentile were 
the most protective (Table 12.2), permitting seagrass survival to depth of 3 m below MLLW in Zone 1 
and 1 m below MLLW in Zone 2 (Figure 12.2). In contrast, model simulations using criteria generated 
from the 75 th percentile were the least protective of seagrass (Table 12.2). The 75 th percentile was 
protective of seagrass to about 2 m below MLLW in Zone 1 but, only maintained seagrass at a depth of 
0 m MLLW in Zone 2 (Figure 12.3). Model simulations for each case and zone are provided in 
Appendix Figures E.3-E.8. 


i f 


91 


Table 12.2 Summary of the protective capacity of different potential criteria derived from 
Yaquina Estuary percentile data. Depth (below MLLW) to which these potential criteria 
permit long-term seagrass persistence and a narrative description for each case and zone. 

Percent change is relative to the median case. 

Case 

Zone 

Protective Capacity 

Depth, m 

% change 


1 

(Median) 

1 

>2 

0 

Present Condition 

2 

>0.5 

0 

2 

(25 th %) 

1 

>3 

+38 

Most Protective 

2 

>1 

+41 

3 

(75 th %) 

1 

>2 

0 

Least Protective 

2 

0 

-48 


Newport 


1 1 Z. marina 

Zone 1 

L- . J 0.2 - -2.0m 

□ < -2.0m 


4 " 5 km 



4" 10 km 


0.2- -0.5m 
< -0.5m 


Zone 1: 



Marine 



Dominated 


Zone 2: 


/ 

Ri\erine 


/ 

Dominated 


/ 

/ 


+ 15 km 


Figure 12.1 Observed eelgrass distribution (yellow cross-hatch) and simulated eelgrass (green) depth 
distribution based on the median case. Brown regions are unsuitable for eelgrass survival. 
Inset boxes show that the median case should maintain current eelgrass distribution in both 
Zones 1 and 2. 


92 











































Newport 


1 1 Z. marina 

Zone I 

LJ 0.2 - -3.0m 
□□ < *3.0m 



Toledo 



+ 10 km 


Dominated 


2 

0.2--1.0m 
< -1.0m 



Zone 2: 
Riverine 
Dominated 


+ 15 km 


Figure 12.2 Observed eelgrass distribution (yellow cross-hatch) and simulated eelgrass (green) depth 
distribution based on the 25 th percentile case. Brown regions are unsuitable for eelgrass 
survival. Inset boxes show that the 25 th percentile case should permit expansion at the lower 
margin of the current eelgrass distribution in both Zones 1 and 2. 


93 


n 































Figure 12.3 Observed eelgrass distribution (yellow cross-hatch) and simulated eelgrass (green) depth 
distribution based on the 75 th percentile case. Brown regions are unsuitable for eelgrass 
survival. Inset boxes show that the 75 th percentile case would eliminate much of the current 
eelgrass distribution in Zone 2. 

12.5 Discussion 

Many seagrass monitoring and assessment programs rely on presence/absence data or periodic 
evaluations of biomass and distribution (Pulich and White, 1997; Berry et al., 2003). However, these 
parameters are not very sensitive indicators of seagrass decline since they require very large sample 
sizes to detect modest changes (Heidelbaugh and Nelson, 1996). For example, biomass is a classic 
response variable; however, by the time monitoring programs can detect changes in biomass the 
perturbation may have caused seagrass decline. Better indicators of stress and decline are required to 
adequately assess seagrass condition. Non-structural carbohydrates may provide a more sensitive and 
integrative response variable since carbohydrate is the energy “currency” of the plant. Our assessment 
of the protective capacity of potential criteria was based on an evaluation of modeled carbohydrate 
content and modeled biomass. 


94 
































Our analysis suggests that the 25 th percentile and median criteria are protective of seagrass in both 
Zones 1 and 2 in the Yaquina Estuary (Table 12.2). Simulations using median kd and DIN values 
project that eelgrass maintains its current depth distribution. The present depth distribution of eelgrass 
in Zone 1 (ocean dominated lower bay) is deeper than in Zone 2 (upper bay) due to greater light 
penetration. The 25 quartile simulations show that the seagrass permanent bed in Zone 1 might be 
extended from the present depth of 2 m to 3 m (below MLLW), but the depth limit in the upper bay 
would not change. Adoption of the 25 th percentile criteria would potentially expand seagrass habitat 
by 38% and 41% in Zones 1 and 2, respectively, relative to the median case. The 75 th percentile 
simulations predict a loss of 48% of habitat in Zone 2 relative to the median case. Most of the change 
occurs at the lower margin since the upper margin was fixed at 0.2 m above MLLW. While these 
changes in seagrass habitat are large in Yaquina Estuary, larger changes might be expected in 
shallower bays, while smaller changes might be expected in systems with steep bathymetric gradients. 

Dry season median Rvalues in the Yaquina Estuary (Table 12.1) are comparable to criteria that 
are currently being used in several other systems including Peconic Bay, NY, Long Island Sound, CT 
and Chesapeake Bay (Table 12.3). The criteria from the other systems are based on the requirements 
for restoration of Z. marina; therefore they may be more restrictive than those required for maintaining 
an existing eelgrass bed (EEA Inc., 1999). The Zone 2 median kj values were similar to the criteria 
values for the restoration of seagrass to depths of 1 m below mean low water (MLW) in Chesapeake 
Bay (Table 12.3). The DIN criteria used by other studies are generally lower than the median values 
observed in the Yaquina Estuary (Table 12.1). DIN values from the 25 th percentile in our study were 
comparable to the concentrations for Chesapeake Bay (Table 12.3). The proposed DIN criteria for 
Long Island Sound and Peconic Bay are much lower than observed DIN levels in the Yaquina Estuary, 
illustrating the importance of regional nutrient criteria. As discussed in Section 3.2, nutrient loading to 
Oregon estuaries is highly dynamic and naturally large as a result of coastal upwelling and alder 
dominated forests. 


95 




Table 12.3 Dry season median light attenuation and DIN values for Yaquina Estuary as 
compared to water quality management targets for other estuaries that are protective of 
eelgrass habitat. 

Location 

kd (m“‘) 

DIN (pM) 

Citation 

Yaquina Estuary, OR 

<0.78 (Zone 1) 
<1.5 (Zone 2) 

14 

This study 

Peconic Bay, NY 2 

<0.75 

1.4 

EEA, Inc. 1999 

Long Island Sound, CT 

<0.7 

2.1 

Holst et al., 2003 

Chesapeake Bay, MD* 3 

<1.5 

10.7 

Batiuk et al., 1992, 2000; 
Wazniak and Hall, 2005 

Chesapeake Bay, MD* 4 

<0.8 

10.7 

Batiuk et al. 1992, 2000; 
Wazniak and Hall, 2005 

1 Based on dry season median values; 2 Based on mean summer values; *Meso and Polyhaline 
portions of the bay during the growth season (April-October); 3 Requirements for restoration 
of seagrass to 1 m MLW depth; 4 Requirements for restoration of seagrass to 2 m MLW depth. 


96 













13. Conclusions and Recommendations 

The Yaquina Estuary is characterized by strong seasonal variation in the magnitude of natural 
nutrient loading and in the dominant nutrient sources. Response variables (particularly, chlorophyll a 
and dissolved oxygen) show similar patterns of seasonal variation. During the wet season, riverine 
nitrogen inputs dominate, while during the dry season oceanic nitrogen sources dominate. There are 
also strong zonal differences in nutrient levels, response variables, and dominant nutrient sources 
within the Yaquina Estuary. In the lower estuary (Zone 1), water quality conditions are strongly 
influenced by ocean conditions, while in the upper portions of the estuary (Zone 2), watershed and 
point source inputs increase in importance. 

3- 

The DIN and PO 4 levels in the Yaquina Estuary would represent medium levels using the 
criteria developed by Bricker et al. (2003) for eutrophication assessment. During the wet season, water 
column DIN levels within the estuary are relatively high. These high nitrogen levels are believed to be 
a naturally high background condition associated with the presence of red alder in the watershed. 

Some portion of the red alder related nitrogen inputs may be related to anthropogenic activities, since 
there may have been changes in red alder distribution related to logging activities in the watershed. 

However, we are presently unable to quantify the relative importance of natural and anthropogenic 

3- 

factors influencing watershed forest composition. During the dry season, PO 4 , NO 3 , chlorophyll a, 
and dissolved oxygen levels in Zone 1 are primarily determined by ocean conditions. There is 
considerable interannual variability in ocean conditions that results from Pacific - scale processes, 
such as El Nino/La Nina and the Pacific-Decadal Oscillation. The high degree of ocean-estuary 
coupling found for Zone 1 within the Yaquina Estuary suggests that monitoring for compliance with 
nutrient criteria in this region may be problematic. For example, hypoxic water and dense 
phytoplankton blooms at times are advected into Zone 1 from the coastal ocean during the dry season. 
Nutrient criteria developed for Zone 1, and any proposed monitoring process to determine compliance, 
would need to take into account this variability in ocean conditions. Distinguishing responses to 
anthropogenic nutrient inputs from those due to natural background variability for such indicators as 
chlorophyll a and dissolved oxygen may be difficult. At a minimum it may require acquisition of 
continuous monitoring data from multiparameter datasondes, an approach which is currently both 
expensive and labor intensive. WED is examining several rapid assessment approaches to allow 


97 


n 


determination of whether conditions in Zone 1 are ocean derived, but these techniques have not yet 
been validated. 

In addition to patterns associated with nutrients, there are strong zonal patterns in turbidity, 

TSS and water clarity within Yaquina Estuary, with increasing turbidity and light attenuation with 
distance from the mouth of the estuary. The lower depth limit of Z. marina habitat becomes shallower 
with distance from the mouth of the estuary, suggesting that light conditions may be influencing the 
distribution of Z. marina particularly within Zone 2 of the estuary. Median values of TSS are not 
considered at this time for inclusion in the list of potential water quality indicators. Comparison of 
spatial and temporal patterns of TSS with other estuaries in Oregon showed inconsistencies for reasons 
that are not clear at present. 

Chlorophyll a levels within the Yaquina Estuary are typically low (median of 2-5 pg f 1 ), and 
would be considered in the Tow’ category when used as an indicator within the NOAA eutrophication 
framework (Bricker et al., 1999) and in the ‘good’ category using the West Coast criteria for water 
quality parameters from the National Coastal Condition Report (US EPA, 2004a). The present Oregon 
criterion (15 pg l' 1 ) is rarely exceeded except in the tidal fresh region (upper Zone 2) where the 
criterion is exceeded frequently during May-August. 

There do not appear to have been major long-term changes in either water column nutrients or 
chlorophyll a within the Yaquina Estuary. Although the Yaquina Estuary experiences dense 
macroalgal blooms during the dry season (particularly in the lower estuary), we do not believe that 
these blooms have increased in frequency, duration or intensity, nor are they likely to be a product of 
cultural eutrophication. Modeling combined with determination of natural abundance, stable isotope 
patterns demonstrated that these macroalgal blooms are primarily fueled by oceanic nitrogen. We 
therefore conclude that green macroalgae biomass is not a useful indicator of cultural eutrophication in 
Yaquina Estuary. 

Comparison of recent and historic Z. marina distributions suggests that there have not been any 
major changes in the last 30 years. The trend analyses did reveal that there was a significant increasing 
trend in DO levels in Zone 2 during the interval of 1960-1984. Review of watershed history suggests 
that current anthropogenic impacts are probably less than they were historically (particularly during 
1960’s-1980’s). 

Assessment of lower depth limits for eelgrass within the Yaquina Estuary allowed estimation of 
the minimum light requirements for sustaining seagrass. The mean light requirement was compared to 


98 



the estimated percentage of surface light available at various depths using the median zonal light 
extinction coefficients (kj). Results indicated that the median kd provided for persistence of seagrass at 
depths within the two estuarine zones that were comparable to current depth distributions, and that 
were consistent with results from the seagrass modeling effort. 

The State of Oregon dissolved oxygen criterion (6.5 mg l' 1 ) is relatively high compared to other 
estuarine DO criteria (see values in U.S. EPA, 2003). The historical record of DO in Zone 2 
demonstrates that this portion of the estuary may be susceptible to DO degradation. Our analyses 
showed that DO levels fall below of the present State of Oregon DO criterion in both Zones 1 and 2, 
but more frequently in Zone 1. We believe these periods of low DO in Zone 1 are related to the import 
of hypoxic water from the coastal ocean into the estuary; however, the causes of the low DO in Zone 2 
are unknown. There are several potential causes of low DO in Zone 2 including import of hypoxic 
ocean water, in situ processes occurring within the estuary, as well as possible effects of WWTF 
effluent discharge. The current Oregon DO criterion should be adequately protective of estuarine 
resources, but is closer to the 25 th percentile value rather than the median value for DO data in Zone 2. 
Using the present numeric Oregon DO criterion, we estimate that between 20 and 30% of 
measurements would not meet the criterion in Zone 2. 

There are still uncertainties with respect to the development of nutrient criteria and the testing 
of potential criteria using the Seagrass Stress-Response Model (SRM). Uncertainties in defining the 
minimum light requirement of eelgrass include the effects of tidal action and temperature. The 
primary input used for the SRM was water clarity, which is affected by multiple factors, including 
turbidity that may be independent of nutrient loads. The SRM did incorporate water column DIN as a 
limiting nutrient, but it did not incorporate indirect nutrient effects, such as relationships between 
nutrient loading and water column chlorophyll a. The SRM does not yet include a term for epiphyte 
effects on light attenuation to seagrass, and thus there is still some uncertainty about precise levels of 
water column light required for maintenance of healthy seagrass. This uncertainty will be resolved in 
future versions of the SRM. 

13.1 Recommendations 

Based on the analyses presented in this report, we suggest that criteria be developed for the wet 
and dry seasons to address the extremely strong seasonal variation in nutrient loads and sources in the 
Yaquina Estuary. Establishment of dry season criteria (May-October) is of first priority since during 

99 


n 


the wet season there appears to be little utilization of nutrients within the estuary, chlorophyll a levels 
are low, and the dissolved oxygen concentrations are high. Because of the high degree of ocean 
influence on water quality parameters that occur in Zone 1 during the dry season and strong tidal 
flushing, a first priority would be the establishment of criteria for Zone 2. An additional justification 
for this prioritization is the potential difficulties in sustaining the data collection needed to differentiate 
natural from anthropogenic nutrient inputs in Zone 1. We suggest that priority for monitoring for 
compliance with any proposed nutrient criterion in the Yaquina Estuary be for Zone 2 in the dry 
season. This is the most likely region and time period where anthropogenic nutrient effects would be 
expressed. 

EPA (2001) summarized that a “Recognized Unique Excellent Condition” estuary would have 
a watershed that is unimpacted with “very little human development, is distant from the influence of 
local population centers, adjacent land uses are undisturbed, and is outside of major atmospheric 
deposition of nitrogen.” With the exception of “adjacent land uses are undisturbed” the Yaquina 
watershed meets all of these conditions. Additionally, from a nutrient (and land use) standpoint we 
believe that the Yaquina watershed is undisturbed compared to many other systems in the U.S., even 
though extensive silviculture occurs in the watershed. 

Following the recommendations in U.S. EPA (2001), median values are the suggested criteria 
for estuaries in “Recognized Unique Excellent Condition” (Table 13.1). The assessment of seagrass 
light requirements in Chapter 8 and the Seagrass Stress-Response Model demonstration in Chapter 12 
indicate that the median percentiles for the water clarity criterion in both Zones would be protective of 
the existing Z. marina habitat in the Yaquina Estuary. The dry season median light attenuation values 
(Table 13.1) for Yaquina Estuary are comparable to water clarity criteria for the protection of 
Z. marina habitat in other estuaries, including Chesapeake Bay, Long Island Sound, and Peconic Bay. 
Since the existing State of Oregon dissolved oxygen criterion is based on a review of physiological 
requirements of biota and appears to be adequately protective of the designated uses, we would 
recommend that the DO criterion remain at this level. 

The present Oregon chlorophyll a criterion is determined as a 3-month average. If the 3-month 
average chlorophyll a levels within the Yaquina Estuary were to approach the present criterion, 
significant trophic shifts in the estuary would be likely. Thus, the current chlorophyll a criterion may 
not prevent some impacts on designated use. A more conservative criterion would be the adoption of 
the medians for chlorophyll a within both Zones 1 and 2 (Table 13.1). 


100 


Table 13.1 Potential dry season criteria for the Yaquina Estuary based on median values for all 
parameters except for DO. 

Parameter (units) 

Zone 1 

Zone 2 

DIN (pM) 

14 

14 

Phosphate (pM) 

1.3 

0.6 

Chlorophyll a (pg l' 1 ) 

3 

5 

Water Clarity (m' 1 ) 

0.8 

1.5 

Dissolved Oxygen (mg l 1 ) 

6.5 


101 


n 











References 

Aller, R.C. 1988. Benthic fauna and biogeochemical processes in marine sediments: the role of burrow 
structures. In: T. H. Blackburn and J. Sorensen (eds.). Nitrogen Cycling in Coastal Marine 
Environments . SCOPE 33. John Wiley, pp. 301-338. 

Amspoker, M.C. 1977. The distribution of intertidal diatoms associated with the sediments of the 
Yaquina Estuary, Oregon. PhD Thesis, Oregon State University, Corvallis, OR. 173 pp. 

Armstrong, D.A., C. Rooper and D. Gunderson. 2003. Estuarine production of juvenile Dungeness 

crab ( Cancer magister ) and contribution to the Oregon-Washington coastal fishery. Estuaries 
26:1174-1188. 

Arnold, G., R. Caldwell, C. Lannan, and J. Winton. 1992. Microbiological quality of the Yaquina 

Estuary. Water Resource Research Institute, Oregon State University, Corvallis, OR. Technical 
Report Project Number G1247-006. 72 pp. 

Batiuk, R.A., R.J. Orth, K.A. Moore, W.C. Dennison, J.C. Stevenson, L.W. Staver, V. Carter, N.B. 
Rybicki, R.E. Hickman, S. Kollar, S. Bieber, and P. Heasley. 1992. Chesapeake Bay 
submerged aquatic vegetation water quality and habitat-based requirements and restoration 
targets: A technical synthesis. United States Environmental Protection Agency, Chesapeake 
Bay Program, Annapolis, MD. 186 pp. 

Batiuk, R.A., B. Bergstrom, E. Koch, L. Murray, J.C. Stevenson, R. Bartleson, V. Carter, N.B. 

Rybicki, J.M. Landwehr, C. Gallegos, L. Karrh, M. Naylor, D. Wilcox, K. Moore, S. Ailstock, 
and M. Teichberg. 2000. Chesapeake Bay submerged aquatic vegetation water quality and 
habitat-based requirements and restoration targets: A second technical synthesis. United States 
Environmental Protection Agency, Chesapeake Bay Program, Annapolis, MD. 174 pp. and 
appendices. Available at: http://www.chesapeakebay.net/pubs/sav/index.html 

Bayer, R.D. 1979. Intertidal zonation of Zostera marina in Yaquina Estuary, Oregon. Syesis 12:147- 
153. 

Bayer, R.D. 1996. Macrophyton and tides at Yaquina Estuary, Lincoln County, Oregon. Journal of 
Oregon Ornithology 6:781-795. 

Berry, H.D., A.T. Sewell, S. Wyllie-Echeverria, B.R. Reeves, T.F. Mumford, J.R. Skalski, R.C. 
Zimmerman, and J. Archer. 2003. Puget Sound submerged vegetation monitoring project: 
2000-2002 monitoring report. Washington State Department of Natural Resources, Aquatic 
Resources Division, Olympia, WA. 


102 





Biebl, R. and C.P. McRoy. 1971. Plasmatic resistance and rate of respiration and photosynthesis in the 
seagrass Zostera marina at different salinities and temperatures. Marine Biology 8:48-56. 

Binkley, D., K. Cromack, and D. Baker, Jr. 1994. Nitrogen fixation by red alder: biology, rates, and 
controls. In: D.E. Hibbs, D.S. DeBell, and R.F. Tarrant (eds). The biology and management of 
red alder . Oregon State University Press, Corvallis, Oregon, pp. 57-72. 

Boese, B.L., K. Alayan, E. Gooch, and B.D. Robbins. 2003. Desiccation index: A direct measure of the 
effects of tidal exposure on eelgrass {Zostera marina ) in an Oregon estuary. Aquatic Botany 
76:329-337. 

Boese, B.L. and B.D.Robbins. Manuscript. Factors affecting the autecology of intertidal eelgrass 
{Zostera marina ) in a North Eastern Pacific Estuary (USA). 

Boese, B.L., B.D. Robbins, and G. Thursby. 2005. Desiccation is a limiting factor for eelgrass {Zostera 
marina L.) distribution in the intertidal zone of northeastern Pacific (USA) estuary. Botanica 
Marina 48:275-283. 

Bolam, S.G., T.F. Fernandes, P. Read, and D. Raffaelli. 2000. Effects of macroalgal mats on intertidal 
sandflats: an experimental study. Journal of Experimental Marine Biology and Ecology 
249:123-137. 

Bonsdorff, E. 1992. Drifting algae and zoobenthos— effects on settling and community structure. 
Netherlands Journal of Sea Research. 30:57-62. 

Bonsdorff, E., E.M. Blomqvist, J. Mattila, and A. Norkko. 1997. Coastal eutrophication: causes, 
consequences and perspectives in the archipelago areas of Northern Baltic Sea. Estuarine, 
Coastal and Shelf Science. 44 (Supplement A):63-72 

Bottom, D.L., B. Kreag, F. Ratti, C. Roye, and R. Starr. 1979. Habitat classification and inventory 
methods for the management of Oregon estuaries. Estuary Inventory Report, Vol 1., Oregon 
Department of Fish and Wildlife, Portland, Or. 

Boyer, E.W., C.L. Goodale, N.A. Jaworski, and R.W. Howarth. 2002. Anthropogenic nitrogen sources 
and relationships to riverine nitrogen export in the northeastern U.S.A. Biogeochemistry 
57/58:137-169. 

Boynton, W.R., W. M. Kemp, and C. W. Keefe. 1982. A comparative analysis of nutrients and other 
factors influencing phytoplankton production. In: V.S. Kennedy (ed.) Estuarine Comparisons . 
Academic Press, New York, NY. pp. 69-90. 


n 


103 





Bricker, S.B., J.G. Ferreira, and T. Simas. 2003. An integrated methodology for assessment of 
estuarine trophic status. Ecological Modeling 169:39-60. 

Bricker, S.B., C.G. Clement, D.E. Pirhalla, S.P. Orlando, and D.R.G. Farrow. 1999. National Estuarine 
Eutrophication Assessment: Effects of Nutrient Enrichment in the Nation’s Estuaries. NOAA, 
National Ocean Service, Special Projects Office and the National Centers for Coastal Ocean 
Science. Silver Spring, MD: 71 pp. 

Brown, C.A. and Ozretich, R.J. Manuscript. Coupling between the coastal ocean and Yaquina Bay, 
Oregon: importance of oceanic inputs relative to other sources. 

Brown, R.F., B.E. Wright, S.D. Riemer, and J. Laake. 2005. Trends in abundance and current status of 
harbor seals in Oregon: 1977-2003. Marine Mammal Science 21:657-670. 

Burgess, R., C. Chancy, D. Campbell, N. Detenbeck, V. Engle, B. Hill, K. Ho, M. Lewis, J. Kurtz, T. 
Norberg-King, P. Pelletier, K. Perez, L. Smith, and V. Snarski. 2004. Classification 
framework for coastal systems. EPA 600/R-04/061. U.S. EPA, Office of Research and 
Development, Research Triangle Park, NC, 66 pp. 

Burke, M.K., W.C. Dennison, and K.A. Moore. 1996. Non-structural carbohydrate reserves of eelgrass 
Zostera marina. Marine Ecology Progress Series 137:195-201. 

Burt, W.V. and W.B. McAlister. 1959. Recent studies in the hydrography of Oregon estuaries. 

Research Briefs, Fish Commission of Oregon, 7:14-27. 

Butler, J.H. 1986. Cycling or reduced trace gases and hydroxylamine in coastal waters. PhD Thesis, 
Oregon State University, Corvallis, OR. 207 pp. 

Cardoso, P.G., A.I. Lillebo, *M.A. Pardal, S.M. Ferreira, and J.C. Marques. 2002. The effect of 
different primary producers on Hydrobia ulvae population dynamics: a case study in a 
temperate intertidal estuary. Journal of Experimental Marine Biology and Ecology 277:173- 
195. 

Cardoso, P.G., M.A. Pardal, D. Raffaelli, A. Baeta, and J.C. Marques. 2004. Macroinvertebrate 

response to different species of macroalgal mats and the role of disturbance history. Journal of 
Experimental Marine Biology and Ecology 308:207-220. 

Choi, B. 1975. Pollution and tidal flushing predictions for Oregon’s estuaries. M.S. Thesis, Oregon 
State University, Corvallis, OR. 163 pp. 

Cloem, J.E. 2001. Our evolving conceptual model of the coastal eutrophication problem. Marine 
Ecology Progress Series 210:223-253. 


104 


Cloera, J.E., E.A. Canuel, and D. Harris. 2002. Stable carbon and nitrogen isotope composition of 
aquatic and terrestrial plants of the San Francisco Bay estuarine system. Limnology and 
Oceanography, 47:713-729. 

Colbert, D. and J. McManus. 2003. Nutrient biogeochemistry in an upwelling-influenced estuary of the 
Pacific Northwest (Tillamook Bay, Oregon, USA). Estuaries 26:1205-1219. 

Cole, T. M. and S. A. Wells. 2000. CE-QUAL-W2: A two-dimensional, laterally-averaged, 

hydrodynamic and water quality model, Version 3, Instruction Report EL-2000. U.S. Army 
Engineering and Research Development Center, Vicksburg, Mississippi. 

Compton, J.E., M.R. Church, S.T. Lamed, and W.E. Hogsett. 2003. Nitrogen export from forested 
watersheds in the Oregon Coast Range: The role of N 2 -fixing red alder. Ecosystems 6:773- 
785. 

Congalton, R. G. and Green, K. 1999. Assessing the Accuracy of Remotely Sensed Data: Principles 
and Practices. Lewis Publishers, New York, NY. 137 p. 

Cortright, R., J. Weber, and R. Bailey. 1987. The Oregon estuary plan book. Oregon Department of 
Land Conservation and Development. Salem, OR. Available at: 
http://www.inforain.org/mapsatwork/oregonestuary/ . 

Corwith, H.L. and P.A. Wheeler. 2002. El Nino related variations in nutrient and chlorophyll 
distributions off Oregon. Progress in Oceanography 54:361-380. 

Cowardin, L.M., V. Carter, F. Golet, and E. LaRoe. 1979. Classification of Wetlands and Deepwater 
Habitats of the United States. U.S. Fish and Wildlife Service. 

Crossett, K.M., T.J Culliton, P.C. Wiley, and T.R. Goodspeed. 2004. Population Trends Along the 
Coastal United States: 1980-2008. Coastal Trends Report Series. National Oceanic and 
Atmospheric Administration. 47 pp. 

Cude, C.G. 1995. Oregon Water Quality Index Report for Mid Coast Basin, Water Years 1986-1995, 
Oregon Department of Environmental Quality, Portland, OR. Available at: 
http://www.deq.state.or.us/lab/wqm/wqindex/mcoast2.htm. 

Cummins, S.P., D.E. Roberts, and K.D. Zimmerman. 2004. Effects of the green macroalga 

Enteromorpha intestinalis on macrobenthic and seagrass assemblages in a shallow coastal 
estuary. Marine Ecology Progress Series 266:77-87. 

Davis, M.W. 1981. Production dynamics of sediment-associated algae in two Oregon estuaries. PhD 
Thesis. Oregon State University, Corvallis, OR. 135 pp. 

105 


n 



De Angelis, M.A. and L.I. Gordon. 1985. Upwelling and river runoff as sources of dissolved nitrous 
oxide to the Alsea estuary, Oregon. Estuarine, Coastal and Shelf Science, 20:375-386. 

De Ben, W.A., W.D. Clothier, G.R. Ditsworth, and D.J. Baumgartner. 1990. Spatio-temporal 

fluctuation in the distribution and abundance of demersal fish and epibenthic crustaceans in 
Yaquina Bay, Oregon. Estuaries 13:469-478. 

den Hartog, C. 1970. Seagrasses of the world . North Holland Publishing Company, Amsterdam. 

den Hartog, C. 1977. Structure, function, and classification in seagrass communities. In: C.P. McRoay 
and C. Heifferich (eds.) Seagrass Ecosystems: A Scientific Perspective . Marcel Dekker Inc., 
New York, NY. pp. 89-121. 

Dennison, W.C. 1987. Effects of light on seagrass photosynthesis, growth and depth distribution. 
Aquatic Botany 27:15-26. 

Dennison, W.C., R.J. Orth, K.A. Moore, J.C. Stevenson, V. Carter, S. Roller, P.W. Bergstrom, and 
R.A. Batiuk. 1993. Assessing water quality with submersed aquatic vegetation. Bioscience 
43:86-94. 

Dettmann, E.H. and J.C. Kurtz. 2006. Proposed Classification Scheme for Coastal Receiving Waters 
Based on SAV and Food Web Sensitivity to Nutrients, Volume 1: Responses of Seagrass and 
Phytoplankton in Estuaries of the Eastern United States to Nutrients: Implications for 
Classification. United States Environmental Protection Agency, National Health and 
Environmental Effects Laboratory, Research Triangle Park, NC. Internal Report AED-06-102. 

DeWitt, T.H., A.F. D’Andrea, C.A. Brown, B.D. Griffen, and P.M. Eldridge. 2004. Impact of 
burrowing shrimp populations on nitrogen cycling and water quality in western North 
American temperate estuaries. In: A. Tamaki (ed.) Proceedings of the Symposium on Ecology 
of Large Bioturbators in Tidal Flats and Shallow Sublittoral Sediments-From Individual 
Behavior to Their Role as Ecosystem Engineers. Nagasaki University, Nagasaki, Japan, pp. 
107-118. 

Dollar, S.J., S.V. Smith, S.M. Vink, S. Obrebski, and J.T. Hollibaugh. 1991. Annual cycle of benthic 
nutrient fluxes in Tomales Bay, California, and contribution to total ecosystem metabolism. 
Marine Ecology Progress Series 79:115-125. 

Driscoll, C. T., D. Whitall, J. Aber, E. Boyer, M. Castro, C. Cronan, C. L. Goodale, P. Groffman, C. 
Hopkinson, K. Lambert, G. Lawrence, and S. Ollinger. 2003. Nitrogen pollution in the 
northeastern United States: sources, effects, and management options. Bioscience 53:357-374. 


106 




Duarte, C.M. 1991. Seagrass depth limits. Aquatic Botany 40:363-377. 

Dugdale, R.C. 1985. The effects of varying nutrient concentration on biological production in 

upwelling regions. California Cooperative Oceanic Fisheries Investigations Reports, Volume 
26, La Jolla, CA. pp. 93-96. 

EEA, Inc. 1999. Peconic estuary program eelgrass criteria study, Volume 1 draft report. Suffolk 
County Department of Health Services, Riverhead, NY. 

Engle, V.D., J.C. Kurtz, L.M. Smith, C. Chancy, and P. Bourgeois. 2007. A Classification of U.S. 
Estuaries Based on Physical and Hydrologic Attributes. Environmental Monitoring and 
Assessment (in publication). 

Everett, R. A. 1991. Intertidal distribution of infauna in a central California lagoon: the role of seasonal 
blooms of macroalgae. Journal of Experimental Marine Biology and Ecology 150:223-247. 

Everett, R.A. 1994. Macroalgae in marine soft-sediment communities: effects of benthic faunal 
assemblages. Journal of Experimental Marine Biology and Ecology 175: 253-274. 

Everett, R.A., G.M. Ruiz, and J.T. Carlton. 1995. Effect of oyster mariculture on submerged aquatic 
vegetation: An experimental test in a Pacific Northwest estuary. Marine Ecology Progress 
Series 123:205-217. 

Eyre, B.D. 2000. Regional evaluation of nutrient transformation and phytoplankton growth in nine 

river-dominated sub-tropical east Australian estuaries. Marine Ecology Progress Series 205:61- 
83. 

Fasten, N. 1931. The Yaquina oyster beds of Oregon. American Naturalist 65:434-468. 

Federal Water Pollution Control Administration. 1966. Reconnaissance Investigation Water Supply 
and Water Quality Control Study Yaquina River Basin, Oregon. United States Department of 
the Interior. 10 pp. 

Ferraro, S.P. and F.A. Cole. 2006. Benthic macrofauna-habitat associations in Willapa Bay, 
Washington, USA. Estuarine, Coastal and Shelf Science (in press). 

Fonseca, M.S. and W.J. Kenworthy. 1987. Effects of current on photosynthesis and distribution of sea 
grasses. Aquatic Botany 27:59-78. 

Fonseca, M.S., W.J. Kenworthy, and G.W. Thayer. 1998. Guidelines for the Conservation and 

Restoration of Seagrasses in the United States and Adjacent Waters. United States Department 
of Commerce, National Oceanic and Atmospheric Administration, Coastal Ocean Program 
Decision Analysis Series No. 12. NO A A Coastal Ocean Office, Silver Spring, MD. 222 pp. 

107 


t t 


Fonseca, M.S., J.C. Zieman, G.W. Thayer, and J.S. Fisher. 1983. The role of current velocity in 

structuring eelgrass ( Zostera marina ) meadows. Estuarine and Coastal Shelf Science 17:367- 
380. 

Forja, J.M. and A.Gomez-Parra. 1998. Measuring nutrient fluxes across the sediment- water interface 
using benthic chambers. Marine Ecology Progress Series 164:95-105. 

Franz, D.R. and I. Friedman. 2002. Effects of a macroalgal mat (Ulva lactuca ) on estuarine sand flat 
copepods: an experimental study. Journal of Experimental Marine Biology and Ecology’ 
271:209-226. 

Frederiksen, M., D. Krause-Jensen, M. Holmer, and J.S. Laursen. 2004. Spatial and temporal variation 
in eelgrass (Zostera marina) landscapes: influence of physical setting. Aquatic Botany 78:147- 
165. 

Frey, B.E. 1977. Ecological survey of the phytoplankton in Yaquina Bay, Oregon, February thru June 
1977. A report to the United States Environmental Protection Agency. 

Fry, B., A. Gace, and J. W. McClelland. 2001. Chemical indicators of anthropogenic nitrogen loading 
in West coast NERR estuaries. Final Report submitted to NOAA/UNH Cooperative Institute for 
Coastal Environmental Technology (CICEET). 79 p. 

Garber, J.H., J.L. Collins, Jr., and M.W. Davis. 1992. Impact of estuarine benthic algal production on 
dissolved nutrients and water quality in the Yaquina River estuary, Oregon. Report WRRI-112, 
Oregon Water Resources Institute, Oregon State University, Corvallis, OR, 58 pp. 

Garono, R. and L. Brophy. 2001. MidCoast Six Field Watershed Assessment. Final Report. Prepared 
for MidCoast Watershed Council. 135 pp. plus appendices Available at: 
http://www.midcoastwatershedcouncil.org. 

Gibson, G.C. 1974. Oyster mortality study- summary report 1966-1972. Fish Commission of Oregon, 
Management and Research Division. United States Department of Commerce, National Marine 
Fisheries Service. 37 pp. 

Gibson, G.C. and D. Snow. 1967. Oyster mortality study, hydrographic data for Yaquina, Coos, and 

Tillamook Bays. United States Department of the Interior, Fish and Wildlife Service, Bureau of 
Commercial Fisheries. 15 pp. 

Grantham, B.A., F. Chan, K.J. Nielsen, D.S. Fox, J.A. Barth, A. Huyer, J. Lubchenco, and B.A. 

Menge. 2004. Upwelling-driven nearshore hypoxia signals ecosystem and oceanographic 
changes in the northeast Pacific. Nature 429:749-754. 


108 



Griffen, B.D., T.H. DeWitt, and C. Langdon. 2004. Particle removal rates by the mud shrimp 

Upogebia pugettensis, its burrow, and a commensal clam: effects on estuarine phytoplankton 
abundance. Marine Ecology> Progress Series 269:223-236. 

Griffin, K. 1997. Eelgrass ecology and commercial oyster cultivation in Tillamook Bay, Oregon. A 
literature review and synthesis. Tillamook Bay National Estuary Project, Garibaldi, OR. 

Elansen, J., A.U. Pedersen, J. Bemtsen , I.S. Ronbog, L.S. Hansen, and B.A. Lomstein. 2000. 

Photosynthesis, respiration and nitrogen uptake by different compartments of a Zostera marina 
community. Aquatic Botany 66:281-295. 

Harding, L.W., Jr. and E.S. Perry. 1997. Long-term increase of phytoplankton biomass in Chesapeake 
Bay, 1950-1994. Marine Ecology Progress Series 157:39-52. 

Hauxwell, J., J. Cebrian, C. Furlong, and I. Valiela. 2001. Macroalgal canopies contribute to eelgrass 
(Zostera marina) decline in temperate estuarine ecosystems. Ecology 82:1007-1022. 

Hauxwell, J., J. Cebrian, and I. Valiela. 2003. Eelgrass Zostera marina loss in temperate estuaries: 
relationship to land-derived nitrogen loads and effect of light limitation imposed by algae. 
Marine Ecology Progress Series 247:59-73. 

Heidelbaugh, S.E, and W.G. Nelson. 1996. An evaluation of core and quadrat methods for assessing 
spatial and temporal changes in seagrass cover. Aquatic Botany 53:227-233. 

Hellblom, F. and M. Bjork. 1999. Photosynthetic responses in Zostera marina to decreasing salinity, 
inorganic carbon content and osmolality. Aquatic Botany 65:97-104. 

Helsel, D.R., D.K. Mueller, and J.R. Slack. 2006. Computer program for the Kendall family of trend 
tests. U.S. Geological Survey Scientific Investigations Report 2005-5275, 4 p. 

Hickey, B.M. and N. Banas. 2003. Oceanography of the U.S. Pacific Northwest coastal ocean and 
estuaries with application to coastal ecology. Estuaries 26:1010-1031. 

Hobbie, E.A., S.A. Macko, and M. Williams. 2000. Correlations between foliar 5 15 N and nitrogen 
concentrations may indicate plant-mycorrhizal interactions. Oecologia, 122, 273-283. 

Hofman, P.A.G. and S.A. de Jong. 1993. Sediment community production and respiration 

measurements: The use of microelectrodes and bell jars. In P.F. Kemp, B.F. Sherr, E.B. Sherr 
and J.J. Cole (eds.), Handbook of Methods in Aquatic Microbial Ecology. Lewis Publishers. 

Holst, L., R. Rozsa, L. Benoit, S. Jacobson, C. Rilling. 2003. Long Island Sound Habitat Restoration 
Initiative. Technical Support for Coastal Habitat Restoration. Section 3: Submerged Aquatic 
Vegetation. Long Island Sound Study, U.S. EPA Long Island Sound Office, Stamford, CT. 


n 


109 


Homer, C., Huang, C., Yang, L., Wylie, B, and Coan, M. 2004. Development of a 2001 national land- 
cover database for the United States. Photogrammetic Engineering & Remote Sensing V ol. 70, 
No. 7: 829-840. 

Howarth, R.W., A. Sharpley, and D. Walker. 2002. Sources of nutrient pollution to coastal waters in 
the United States: Implications for achieving coastal water quality goals. Estuaries 25:656- 
676. 

Howerton, J. 1984. Key mammals of the Columbia River estuary. Columbia River Estuary Data 
Development Program, Washington Department of Game, Habitat Management Division, 
Olympia, WA. 

Hughes, D.J., R.J.A. Atkinson, and A.D. Ansell. 2000. A field test of the effects of megafaunal 

burrows on benthic chamber measurements of sediment-water solute fluxes. Marine Ecology 
Progress Series 195:189-199. 

Hull, S.C. 1987. Macroalgal mats and species abundance: a field experiment. Estuarine, Coastal and 
Shelf Science 25:519-532. 

Isaksson, I. and L. Pihl. 1992. Structural changes in benthic macrovegetation and associated epibenthic 
faunal communities. Netherlands Journal of Sea Research 30:131 -140. 

Isaksson, I., L. Pihl, and J. van Montfrans. 1994. Eutrophication-related changes in macrovegetation 
and foraging of young cod ( Gadus morhua L.): a mesocosm experiment. Journal of 
Experimental Marine Biology and Ecology 177:203-217. 

Johnson, J.K. 1980. Population dynamics and cohort persistence of Acartia californiensis 

(Copepoda:Calanoida) in Yaquina Bay, Oregon. PhD Thesis, Oregon State University, 
Corvallis, OR. 305 pp. 

Jones, M. and E. Pinn. 2006. The impact of a macroalgal mat on benthic biodiversity in Poole Harbour. 
Marine Pollution Bulletin 53: 63-71. 

Kaldy, J.E. 2006a. Carbon, nitrogen, phosphorous and heavy metal budgets: How large is the 

eelgrass ( Zostera marina L.) sink in a temperate estuary? Marine Pollution Bulletin 52: 332- 
356. 

Kaldy, J.E. 2006b. Production ecology of the non-indigenous seagrass, dwarf eelgrass ( Zostera 
japonica Ascher. & Graeb.), in a Pacific Northwest estuary. Hydrobiologia 553:201-217. 


110 


Kaldy, J.E. and P.M. Eldridge (eds.). 2006. Development of mechanistic models to guide 

establishment of protective criteria for seagrasses. Report on the Zostera marina and Thalassia 
testudinum stress-response models. Office of Research and Development, National Health and 
Environmental Effects Research Laboratory, EPA Internal Report. 

Karentz, D. 1975. The distribution of planktonic diatoms in Yaquina Estuary, Oregon. M.S. Thesis, 
Oregon State University, Corvallis, OR. 137 pp. 

Karentz, D. and C.D. Mclntire. 1977. Distribution of diatoms in the plankton of Yaquina Estuary, 
Oregon. Journal of Phycology 13:379-388. 

Kentula, M.E. and T.H. DeWitt. 2003. Abundance of seagrass {Zostera marina L.) and macroalgae in 
relation to the salinity-temperature gradient in Yaquina Bay, Oregon. Estuaries 26:1130-1141. 

Kentula, M.E. and D.C. Mclntire. 1986. The autecology and production dynamics of eelgrass {Zostera 
marina L.) in Netarts Bay, Oregon. Estuaries 9:188-199. 

Kjeldsen, C.K. 1967. Effects of variations in salinity and temperature on some estuarine macro-algae. 
Dissertation. Oregon State University, Corvallis, OR. 157 pp. 

Koch, E.W. and S. Beer. 1996. Tides, light and the distribution of Zostera marina in Long Island 
Sound, USA. Aquatic Botany 53:97-107. 

Krause-Jensen, D., M.F. Persen, and C. Jensen. 2003. Regulation of eelgrass {Zostera marina ) cover 
depth gradients in Danish coastal waters. Estuaries 26:866-877. 

Kristensen, E. 1988. Benthic fauna and biogeochemical processes in marine sediments: microbial 
activities and fluxes. In: T.H. Blackburn and J. Sorensen (eds.) Nitrogen cycling in coastal 
marine environments . John Wiley and Sons, Chichester, pp. 275-299. 

Kristensen, E., M.H. Jensen, and R.C. Aller. 1991. Direct measurement of dissolved inorganic nitrogen 
exchange and denitrification in individual polychaete {Nereis virens) burrows. Journal of 
Marine Research 49:355-377. 

Kulm, L.D. and J.V. Byrne. 1966. Sedimentary response to hydrography in an Oregon estuary. 
Marine Geology 7 4:85-188. 

Kurtz, J.C., N.D. Detenbeck, V.D. Engle, K. Ho, L.M. Smith, S.J. Jordan, and D. Campbell. 2006. 

Classifying coastal waters: Current necessity and historical perspective. Estuaries and Coasts 
29(1): 107-123. 

Landis, J. R. and Koch, G. C. 1977. The measurement of observer agreement for categorical data. 
Biometrics 33, 159-174. 


n 


111 




Lamed, S.T. 2003. Effects of the invasive, nonindigenous seagrass Zoster a japonica on nutrient fluxes 
between the water column and benthos in a NE Pacific estuary. Marine Ecology Progress 
Series 254:69-80. 

Lauringson, V. and J. Kotta. 2006. Influence of the thin drift algal mats on the distribution of 
macrozoobenthos in Koiguste Bay, NE Baltic Sea. Hydrobiologia 554: 97-105. 

Lee, H. II, C.A. Brown, B.L. Boese, and D.R. Young (eds.). 2006. Proposed Classification Scheme for 
Coastal Receiving Waters Based on SAV and Food Web Sensitivity to Nutrients, Volume 2: 
Nutrient Drivers, Seagrass Distributions, and Regional Classifications of Pacific Northwest 
Estuaries, United States Environmental Protection Agency Report, Office of Research and 
Development, National Health and Environmental Effects Laboratory. Internal Report. 

Levings, C.D. 1990. Strategies for restoring and developing fish habitats in the Strait of Georgia-Puget 
Sound inland sea, Northeast Pacific Ocean. Marine Pollution Bulletin 23:417-422. 

Lewis, L.J. and T. C. Kelly. 2001. A short-term study of the effects of algal mats on the distribution 
and behavioural ecology of estuarine birds. Bird Study 48:354-360. 

Lewis, L.J., J. Davenport, and T.C. Kelly. 2003. Responses of benthic invertebrates and their avian 
predators to the experimental removal of macroalgal mats. Journal of the Marine Biological 
Association of the United Kingdom 83:31-36. 

Lopes, R.J., M.A. Pardal, and J.C. Marques. 2000. Impact of macroalgal blooms and wader predation 
on intertidal macroinvertebrates: experimental evidence from the Mondego estuary (Portugal). 
Journal of Experimental Marine Biology and Ecology 249:165-179. 

Madden, C.J. and W.M. Kemp. 1996. Ecosystem model of estuarine submersed plant community: 
Calibration and simulation of eutrophication responses. Estuaries 19:457-474. 

Marinelli, R.L. and T.J. Williams. 2003. Evidence for density dependent effects of infauna on sediment 
biogeochemistry and benthic pelagic coupling in nearshore systems. Estuarine, Coastal and 
Shelf Science 57:179-192. 

Matson, A.L. 1964. Dissolved Silicate in Waters Offshore Oregon and in Four Adjacent Rivers. MS 
Thesis, Oregon State University, Corvallis, OR. 98 pp. 

Merrifield, K. 1998. Waterbird censuses of Yaquina Bay, Oregon: March 1993-February 1994. Oregon 
Department of Fish and Wildlife, Technical Report 98-1-01. Corvallis, OR. 

Monbet, Y. 1992. Control of phytoplankton biomass in estuaries: A comparative analysis of 
microtidal and macrotidal estuaries. Estuaries 15:563-571. 


112 


Mrazik, S. 2006. Oregon Water Quality Index Summary Report Water Years 1996-2005. State of 
Oregon Department of Environmental Quality, Portland, OR. DEQ06-LAB-0029-TR. 

Murias, T., J.A. Cabral, J.C. Marques, and J.D. Goss-Custard. 1996. Short-term effects of intertidal 

macroalgal blooms on the macrohabitat selection and feeding behaviour of wading birds in the 
Mondego estuary (West Portugal). Estuarine, Coastal and Shelf Science 43:677-688. 

National Atmospheric Deposition Program (NRSP-3)/National Trends Network. 2003. NADP Program 
Office, Illinois State Water Survey, 2204 Griffith Dr., Champaign, IL 61820. 

National Audubon Society. 2002. The Christmas Bird Count Historical Results [Online]. Available at: 
http://www.audubon.org/bird/cbc [November 4, 2006]. 

Naymik, J., Y. Pan, and J. Ford. 2005. Diatom assemblages as indicators of timber harvest effects in 
coastal Oregon streams. Journal of the North American Benthological Society 24:569-584. 

Nelson, W.G. 2005. Seagrass Protective Criteria: A Review and Assessment of Research Status. 

United States Environmental Protection Agency, Office of Research and Development, 

National Health and Environmental Effects Research Laboratory, Research Triangle Park, NC. 
Internal Report. 

Nelson, W.G., H. Lee II, J.O. Lamberson, V. Engle, L. Harwell, and L.M. Smith. 2004. Condition of 
Estuaries of Western United States for 1999: A Statistical Summary. United States 
Environmental Protection Agency, Office of Research and Development, National Health and 
Environmental Effects Research Laboratory, Research Triangle Park, NC. EPA/620/R-04/200. 

Nicholls, D.J., C.R. Tubbs, and F.N. Haynes. 1981. The effect of green algal mats on 
intertidal macrobenthic communities. Kiefer Meerestorsch Sonderh 5:551-520. 

Nielsen, S.L., J. Borum, O. Geertz-Hansen, and J. Sand-Jensen. 1989. Marine bundplanters 
dybdegraense. Vand Miljo. 5: 217-220. 

Norkko, A. 1998. The impact of loose-lying algal mats and predation by the brown shrimp Crangon 
crangon (L.) on infaunal prey dispersal and survival. Journal of Experimental Marine Biology 
and Ecology 221:99-116. 

Norkko, A. and E. Bonsdorff. 1996a. Rapid zoobenthic community responses to accumulations of 
drifting algae. Marine Ecology Progress Series 131:143-157. 

Norkko, A. and E. Bonsdorff. 1996b. Population responses of coastal zoobenthos to stress induced by 
drifting algal mats. Marine Ecology Progress Series 140:141 -151. 


n 


113 



Norkko, A. and E. Bonsdorff. 1996c. Altered benthic prey-availability due to episodic oxygen 
deficiency caused by drifting algal mats. Marine Ecology 17:355-372. 

Norkko, J., E. Bonsdorff, and A. Norkko. 2000. Drifting algal mats as an alternative habitat for benthic 
invertebrates: species specific responses to a transient resource. Journal of Experimental 
Marine Biology and Ecology 248:79-104. 

ODFW (Oregon Department of Fish and Wildlife). 2006. State of Oregon Conservation Plan for the 
Oregon Coast Coho Evolutionarily Significant Unit. Public Draft dated October 6, 2006, 
prepared by ODFW in partnership with state and federal natural resource agencies. Available 
at: http://www.oreuon-pjan.org/OPSW/cohoproiect/Public Draft . 

Oregon Department of Agriculture . 2000-2005. Oyster plat production annual report. Oregon 
Department of Agriculture, Natural Resources Division, Salem, OR. 

Oregon Department of Environmental Quality. 1995. Dissolved Oxygen, 1992-1994 Water Quality 
Standards Review. Department of Environmental Quality Standards and Assessment Section, 
Portland, Oregon. 

Olafsson, E.B. 1988. Inhibition of larval settlement to a soft bottom benthic community by drifting 
algal mats: an experimental test. Marine Biology 97:571-574. 

Orr, A.J., A.S. Banks, S. Mellman, FI.R. Huber, R.L. DeLong, and R.F. Brown. 2004. Examination of 
the foraging habits of Pacific harbor seal ( Phoca vitu/ina richardsi ) to describe their use of the 
Umpqua River, Oregon, and their predation on salmonids. Fishery Bulletin 102:108-117. 

Orth, R.J. 1992. A perspective on plant-animal interactions in seagrasses: physical and biological 

determinants influencing plant and animal abundance. In: D.M. John, S.J. Hawkins, and J.H. 
Price (eds.) Plant-Animal Interactions in the Marine Benthos . Clarendon Press, Oxford, pp. 
147-164. 

Osterling, M. and L. Pihl. 2001. Effects of filamentous green algal mats on benthic macrofaunal 

functional feeding groups. Journal of Experimental Marine Biology and Ecology 263:159-183. 

PSMFC (Pacific States Marine Fisheries Commission). 2006. Recreational Fisheries Information 
Network (RecFIN), Download Sample Data Files. Retrieved November 2, 2006 from the 
RecFIN database on the World Wide Web: http://www.psmfc.org/recfm/data.htm . 

Papaspyrou, S., M. Thessalou-Legaki, and E. Kristensen. 2004. Impact of Pestarella tyrrhena on 

benthic metabolism in sediment microcosms enriched with seagrass and macroalgal detritus. 
Marine Ecology Progress Series 281:165-179. 


114 





Pardal, M.A., J.C. Marques, I. Metelo, A.I. Lilleboand M.R. Flindt. 2000. Impact of eutrophication on 
the life cycle, population dynamics and production of Ampithoe valida (Amphipoda) along an 
estuarine spatial gradient (Mondego estuary, Portugal). Marine Ecology Progress Series 
196:207-219. 

Percy, K.L., D.A. Bella, C. Sutterlin, and P.C. Klingeman. 1974. Descriptions and information sources 
for Oregon estuaries. Sea Grant College Program, Oregon State University, Corvallis, Oregon. 
294 pp. 

Phillips, R.C. 1984. The ecology of eelgrass meadows of the Pacific Northwest coast: a community 
profile. United States Department of the Interior, Fish and Wildlife Service. FWS/OBS-84/24. 
85 pp. 

Pihl, L. and H.W. van der Veer. 1992. Importance of exposure and habitat structure for the population 
density of O-Group Plaice, Pleuronectes platessa L., in coastal nursery areas. Netherlands 
Journal of Sea Research 29:145-152. 

Pihl, L., H. Wennhage, and S. Nilsson. 1994. Fish assemblage structure in relation to macrophytes and 
filamentous epiphytes in shallow non-tidal rocky- and soft-bottom habitats. Environmental 
Biology of Fishes. 39: 271-288. 

Pihl, L., I. Isaksson, H. Wennhage, and PO. Moksnes. 1995. Recent increase of filamentous algae in 
shallow Swedish bays: effects on the community structure of epibenthic fauna and fish. 
Netherlands Journal of Aquatic Ecology 29:349-358. 

Pulich, W. and W.A. White. 1997. Current status and historical trends of seagrasses in the Corpus 
Christi Bay national estuary program study area. Texas Natural Resource Conservation 
Commission, Austin, Texas. CCBNEP-20. 

Quinn, H., D.T. Lucid, J.P. Tolson, C. J. Klein, S. P. Orlando, and C. Alexander. 1991. Susceptibility 
and status of West coast estuaries to nutrient discharges: San Diego Bay to Puget Sound. 
Summary Report. United States Department of Commerce, National Oceanic and Atmospheric 
Administration and the United States Environmental Protection Agency. Rockville, MD. 35 pp. 

Raffaelli, D., J. Limia, S. Hull, and S. Pont. 1991. Interactions between the amphipod Corophium 
volutator and macroalgal mats on estuarine mudflats. Journal of the Marine Biological 
Association of the United Kingdom. 71:899-908. 

Raffaelli, D., S. Hull, and H. Milne. 1989. Long-term changes in nutrients, weed mats and shorebirds 
in an estuarine system. Cahiers de Biologie Marine 30:259-270. 


11- 


115 


Raffaelli, D.G., J.A. Raven, and L J. Poole. 1998. Ecological impact of green macroalgal blooms. 

Oceanography and Marine Biology: an Annual Review 36:97-125. 

Riznyk, R.Z. and H.K. Phinney. 1972. Manometric assessment of interstitial microalgae production in 
two estuarine sediments. Oecologia 10:192-203. 

Robbins, B.D. and B.L. Boese. 2002. Macroalgae Volume: a Surrogate for Biomass in Some Green 
Algae. Botanica Marina , 45:586-588. 

Roegner, G.C., B.M. Hickey, J.A. Newton, A.L. Shanks, and D.A. Armstrong. 2002. Wind-induced 
plume and bloom intrusions into Willapa Bay, Washington. Limnology and Oceanography 
47:1033-1042. 

Roegner, G. and A. Shanks. 2001. Import of coastally-derived chlorophyll a to South Slough, Oregon. 
Estuaries 24:224-256. 

Rossi, F. 2006. Small-scale burial of macroalgal detritus in marine sediments: effects of Ulva spp. On 
the spatial distribution of macrofauna assemblages. Journal of Experimental Marine Biology 
and Ecology 332:84-95. 

Sedell, J.R. and W.S. Duval. 1985. Influence of forest and rangeland management on anadromous fish 
habitat in western North America, water transportation and storage of logs. United States 
Department of Agriculture, Forest Service, Pacific Northwest Forest and Range Experiment 
Station.General Tech. Report PNW-186. 

Sedell, J.R., N. Leone, and W.S. Duval. 1991. Water Transportation and Storage of Logs. In: 

Influences of Forest and Rangeland Management on Salmonid Fishes and Their Habitats . 

American Fisheries Society Special Publication 19:325-368. 

Seliskar, D.M. and J.L. Gallagher. 1983. The ecology of tidal marshes of the Pacific Northwest coast: a 
community profile. United States Department of the Interior, Fish and Wildlife Service, 
FWS/OBS-82/32. 65 pp. 

Sewell A.T., J.G. Norris, and S. Wyllie-Echeverria. 2001. Eelgrass monitoring in Puget Sound: 

Overview of the submerged vegetation monitoring project. Available at: www.psat.wa.gov . 
Shirzad, F.F., S.P Orlando., C.J. Klein, S.E. Holliday, M.A. Warren, and M.E. Monaco. 1988. National 
estuarine inventory: Supplement 1, Physical and Hydrologic characteristics, The Oregon 
estuaries. United States Department of Commerce, National Oceanic and Atmospheric 
Administration, Rockville, MD. 


116 





Short, F.T., D.M. Burdick, and J.E. Kaldy. 1995. Mesocosm experiments quantify the effects of 
eutrophication on eelgrass, Zostera marina. Limnology and Oceanography 40:740-749. 

Sigleo, A.C. and W.E. Frick. 2007. Seasonal variations in river discharge and nutrient export to a 
Northeastern Pacific estuary. Estuarine, Coastal and Shelf Science 73, 368-378 

Sigleo, A.C., C.W. Mordy, P. Stabeno, and W.E. Frick. 2005. Nitrate variability along the Oregon 
coast: Estuarine-coastal exchange. Estuarine, Coastal and Shelf Science 64:211-222. 

Simenstad, C.A. 1983. The ecology of estuarine channels of the Pacific Northwest coast: a community 
profile. United States Department of the Interior, Fish and Wildlife Service, FWS/OBS-83/05. 

181 pp. 

Simenstad, C.A. and R.C. Wissar. 1985. C evidence of the origins and fates of organic carbon in 
estuarine and nearshore food webs. Marine Ecology Progress Series 11:141-152. 

Smith, V. H. 2006. Responses of estuarine and coastal marine phytoplankton to nitrogen and 
phosphorous enrichment. Limnology and Oceanography 51:377-384. 

Soetaert, K., J.J. Middleburg, C. Heip, P. Meire, S. Van Damme, and T. Maris. 2006. Long-term 
change in dissolved inorganic nutrients in the heterotrophic Scheldt estuary (Belgium, The 
Netherlands). Limnology and Oceanography 51(1, part 2): 409-423. 

Sokal, R.R. and F. J. Rholf. 1981. Biometry. 2 nd Edition. W.H. Freeman and Company, New York. 

Soulsby, P.G., D. Lowthion, and M. Houston. 1982. Effects of macroalgal mats on the ecology of 
intertidal mudflats. Marine Pollution Bulletin 13:162-166. 

Specht, D.T. 1975. Seasonal variation of algal biomass production potential and nutrient limitation in 
Yaquina Bay, Oregon. Presented at Biostimulation and Nutrient Assessment Symposium, Utah 
State University, Logan, Utah, September 10-12, 1975. 

State Water Resources Board, Salem, Oregon, 1965. Mid-coast Basin. Salem, Oregon. 

Sundback, K., A. Miles, and E. Goransson. 2000. Nitrogen fluxes, denitrification and the role of 

microphytobenthos in microtidal shallow-water sediments: an annual study. Marine Ecology 
Progress Series 200:59-76. 

Sundback, K., A. Miles, S. Hulth, L. Pihl, P. Engstrom, E. Selander, and A. Svenson. 2003. Importance 
of benthic nutrient regeneration during initiation of macroalgal blooms in shallow bays. Marine 
Ecology Progress Series 246:115-126. 


117 


i ^ 


Tappin, A. D. 2002. An examination of the fluxes of nitrogen and phosphorous in temperate and 

tropical estuaries: Current estimates and uncertainties. Estuarine, Coastal and Shelj Science 
55:885-901. 

Thayer G.W., D.A. Wolfe, and R.B. Williams. 1975. The impact of man on seagrass systems. 
American Scientist 63:288-296. 

The Research Group. 2005. Oregon marine recreational fisheries economic contributions in 2004. 

Prepared for Oregon Department of Fish and Wildlife and Oregon Coastal Zone Management 
Association. 

Thiel, M. and L. Watling. 1998. Effects of green algal mats on infaunal colonization of a New England 
mud flat - long-lasting but highly localized effects. Hvdrobiologia 375/376:177-189. 

Thom, R.M., L.D. Antrim, A.B. Borde, W.W. Gardiner, D.K. Shreffler, P.G. Farley, J.G. Norris, S. 
Wyllie Echeverria, and T.P. McKenzie. 1998. Puget Sound’s eelgrass meadows Factors 
contributing to depth distribution and spatial patchiness. Puget Sound Research ’98 
Proceedings. March 12-13, 1998 Seattle, WA. 

Thom, R.M., T.L. Parkwell, D.K. Niyogi, and D.K. Shreffler. 1994. Effects of graveling on the 

primary productivity, respiration and nutrient flux of two estuarine tidal flats. Marine Biology 
118:329-341. 

Thrush, S.F. 1986. The sublittoral macrobenthic community structure of an Irish sea-lough: effect of 
decomposing accumulations of seaweed. Journal of Experimental Marine Biology and Ecology 
96:199-212. 

Tjepkema, J.D., C.R. Schwintzer, R.H. Burris, G.V. Johnson, and W.B. Silvester. 2000. Natural 
abundance of 15 N in actinorhizal plants and nodules. Plant and Soil 219:285-289. 

Tubbs, C.R. 1977. Wildlife and waders in Langstone Harbour. British Birds 70:177-199. 

Tubbs, C.R. and J.M. Tubbs. 1980. Wader and Shelduck feeding distribution in Langstone Harbour, 
Hampshire. Bird Study 27:239-248. 

Tubbs, C.R. and J.M. Tubbs. 1983. Macroalgal mats in Langstone Harbour, Hampshire, England. 
Marine Pollution Bulletin 14:148-149. 

Underwood, G.J.C., and J. Kromkamp. 1999. Primary production by phytoplankton and 

microphytobenthos in estuaries. In: D.B. Nedwell and D.G. Raffaelli (eds.). Nutrients in 
estuaries . Academic Press, N.Y., pp. 93-153. 

U.S. Environmental Protection Agency. 2000. Ambient Water Quality Criteria Recommendations. 


118 




Information Supporting the Development of State and Tribal Nutrient Criteria. Rivers and 
Streams in Nutrient Ecoregion II. Office of Water. EPA-822-B-00-015. 

U.S. Environmental Protection Agency. 2001. Nutrient Criteria Technical Guidance Manual. Estuarine 
and Coastal Marine Waters. Office of Water. EPA-822-B-01-003. 

U.S. Environmental Protection Agency. 2002. Aquatic Stressors: Framework and Implementation Plan 
for Effects Research. United States Environmental Protection Agency, National Health and 
Human Effects Research Laboratory, Research Triangle Park, NC. EPA 600/R-02/074. 

U.S. Environmental Protection Agency. 2003. Ambient Water Quality Criteria for Dissolved Oxygen, 
Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries. Region III 
Chesapeake Program Office & Region III Water Protection Division. EPA-903-R-03-002. 

U.S. Environmental Protection Agency. 2004a. National Coastal Condition Report II. Office of 
Research and Development/Office of Water, Washington, DC. EPA-620/R-03/002. 

U.S. Environmental Protection Agency. 2004b. Analytical Tools Interface for Landscape Assessments 
(ATtILA). User Manual. EPA/600/R-04/083. 2004 Version. 39 p. 
Website: http:/Ayww.epa.gov/esd/land-sei/attila/pdf/user guide.pdf 

USFWS (U. S. Fish and Wildlife Service). 1968. Fish and wildlife of Yaquina Bay, Oregon: 

preliminary survey of fish and wildlife in relation to the ecological and biological aspects of 
Yaquina Bay, Oregon. United States Department of Interior, Fish and Wildlife Service. 

Valiela, I., M. Geist, and G. Tomasky. 2000a. Nitrogen loading from watersheds to estuaries: 
verification of the Waquoit Bay nitrogen loading model. Biogeochemistry 49:277-293. 

Valiela, I., G. Tomasky, J. Hauxwell, M.L. Cole, J. Cebrian, and K.D. Kroeger. 2000b. 

Operationalizing sustainability: management and risk assessment of land-derived nitrogen 
loads to estuaries. Ecological Applications 10(4): 1006-1023. 

Vimstein, R.W., E.W. Carter IV, L.J. Morris, and J.D. Miller. 2002. Utility of seagrass restoration 

indices based on area, depth, and light. In: H.S. Greening (ed.), Seagrass Management: It’s Not 
Just Nutrients! Tampa Bay Estuary Program, proceedings of a symposium. St. Petersburg, FL. 
pp.69-80. 

Waslenchuk, D.G., E.A. Matson, R.N. Zajac, F.C. Dobbs, and J.M. Tramontano. 1982. Geochemistry 
of burrow waters vented by a bioturbating shrimp in Bermudian sediments. Marine Biology 
72:219-225. 




119 



Wazniak, C.E. and M.R. Hall (eds.) 2005. Maryland's coastal bays: ecosystem health assessment 2004. 
Maryland Department of Natural Resources, Tidewater Ecosystem Assessment, Annapolis, 
MD. DNR-12-1202-0009. 

Webb, A.P. and B.D. Eyre. 2004. Effect of natural populations of burrowing thalassinidean shrimp on 
sediment irrigation, benthic metabolism, nutrient fluxes and denitrification. Marine Ecology 
Progress Series 268: 205-220. 

Welsh, D.T. 2003. It’s a dirty job but someone has to do it: the role of marine benthic macrofauna in 
organic matter turnover and nutrient recycling to the water column. Chemistry and Ecology 19: 
321-342. 

Wennhage, H. and L. Pihl. 1994. Substratum selection by juvenile plaice (Pleuronectes platessa L.): 
impact of benthic microalgae and filamentous macroalgae. Netherlands Journal of Sea 
Research 32:343-351. 

Wheeler, P.A., J. Huyer, and J. Fleischbein. 2003. Cold halocline, increased nutrients and higher 
productivity off Oregon in 2002. Geophysical Research Letters 30: Art. No. 8021. 

Wigington, P.J., Jr., M.R. Church, T.C. Strickland, K.N. Eshleman, and J. van Sickle. 1998. Autumn 
chemistry of Oregon coast range streams, Journal of the American Water Resources 
Association 34:1035-1049. 

Yang, L, C. Huang, C.G. Homer, B.K. Wylie, and M.J. Coan. 2003. An approach for mapping large- 
area impervious surfaces: synergistic use of Landsat-7 ETM+ and high spatial resolution 
imagery. Can. J. Remote Sensing , 29:230-240. 

Young, D.R., S.P. Cline, D.T. Specht, P.J. Clinton, B.D. Robbins, and J.O. Lamberson. 2000. Mapping 
spatial/temporal distributions of green macroalgae in a Pacific Northwest coastal estuary via 
small format color infrared aerial photography. Proceedings of the Sixth International 
Conference on Remote Sensing for Marine and Coastal Environments. Charleston, SC. May 1- 
3, 2000. 

Young, D.R., Clinton, P.J., and Specht, D.T. In Review. Mapping intertidal eelgrass ( Zostera marina 
L.) in three coastal estuaries of the Pacific Northwest USA using false-color near-infrared aerial 
photography. Submitted to International Journal of Remote Sensing. 

Young, D.R., D.T. Specht, P.J. Clinton, and H. Lee, IT 1998. Use of color infrared aerial photography 
to map distributions of eelgrass and green macroalgae in a non-urbanized estuary of the Pacific 


120 


Northwest USA. Proceedings of the Fifth International Conference on Remote Sensing for 
Marine and Coastal Environments. San Diego, CA. October 5-7, 1998. 

Young, D.R., D.T. Specht, B.D. Robbins, and P.J. Clinton. 1999. Delineation of Pacific Northwest 

SAVs from aerial photography: natural color or color infrared film? Proceedings of the Annual 
Conference of the American Society of Photogrammetry and Remote Sensing, Bethesda, MD. 
pp.1173-1178. 

Zimmerman, R.C. and R.S. Alberte. 1996. Effect of light/dark transition on carbon translocation in 
eelgrass Zostera marina seedlings. Marine Ecology Progress Series 136:305-309. 

Zimmerman R.C., R. Smith, and R.S. Alberte. 1989. Thermal acclimation and whole-plant carbon 

balance in Zostera marina L. (eelgrass). Journal of Experimental Marine Biology and Ecology 
130:93-109. 

Zimmerman, R.C., J.L. Reguzzoni, and R.S. Alberte. 1995. Eelgrass (Zostera marina L.) transplants in 
San Francisco Bay: Role of light availability on metabolism, growth and survival. Aquatic 
Botany 51:67-87. 


121 


n 


Appendix A: Benthic Processes in Yaquina Estuary 

Benthic communities harboring actively burrowing, tube- or burrow-dwelling infaunal are 
often associated with elevated rates of DIN advection from sediments (e.g., Aller, 1988; Kristensen et 
al., 1991; Marinelli and Williams, 2003). Bioturbation and bioirrigation by infauna oxygenates 
sediments and mixes labile organic matter into sediments, stimulating the activity of microbial 
communities responsible for recycling of nutrients (Kristensen, 1988; Welsh, 2003). Benthic fauna 
consume organic matter from the water column (i.e., filter feeders), at the sediment surface (i.e., 
herbivores, surface deposit-feeders, carnivores), or below the sediment surface (i.e., sub-surface 
deposit feeders, carnivores). The presence of seagrasses (i.e., Zostera spp.), green macroalgae (i.e., 
Enteromorphci spp. and Ulva spp.), or microphytobenthic algae usually results in a net benthic uptake 
of DIN during daylight hours (Underwood and Kromkamp, 1999; Hansen et al., 2000; Sundback et al., 
2000; Sundback et al., 2003). Benthic primary producers are recycled into the benthos (i.e., consumed 
by benthic herbivores or surface deposit feeders, buried by bioturbators, decay at the sediment surface) 
or are transported out of the estuary by currents. 

Five studies of benthic nutrient flux have been conducted in Pacific estuaries north of San 
Francisco, but the reported benthic flux-chamber data in four (i.e., Dollar et al. 1991; Garber et al. 

1992; Thom et al. 1994; Lamed 2003) may not be appropriate for the purpose of estimating estuary- 
scale nutrient fluxes in Yaquina Estuary because they did not adequately take into account the presence 
of thalassinid burrowing shrimp. As described in Section 3.2.3 Benthic Processes, previous studies of 
benthic nutrient flux in PNW estuaries may have underestimated the nutrient fluxes from sediments by 
failing to account for the presence of burrowing shrimp. Both ghost shrimp and mud shrimp were 
present, or were likely to have been present, at field sites in all of the studies. Burrowing shrimp are 
very common, and frequently very abundant, in NE Pacific estuaries (DeWitt et al., 2004 and 
references therein), and they construct deep (>50 cm), branching burrows with openings 10’s of cm 
apart. These shrimp are prodigious bioturbators, actively irrigate their burrows, and thus greatly 
elevate nutrient flux from sediments (Waslenchuk et al., 1982; DeWitt et al., 2004; Webb and Eyre, 
2004; Papaspyrou et al., 2004). 

Failure to take the presence of burrowing shrimp into account can result in water inside the 
chamber being exchanged with water outside of the chamber via shrimp burrows (e.g., Hughes et al. 
2000), which violates the requirement that benthic chambers be closed microcosms (Hofman and de 
Jong, 1993, Forja and Gomez-Parra 1998). In the case of one study, benthic chamber treatments 


122 


deliberately excluded shrimp (Thom et al., 1994), and while these measurements would represent tide 
flat habitats that have no shrimp, such areas are only 16% of the total Yaquina tide flat area (DeWitt et 
al., 2004). Thus, because of these uncertainties, benthic flux measurements from those four studies 
(Dollar et al. 1991; Garber et al. 1992; Thom et al. 1994; Lamed 2003) were not used for estimating 
the estuary-scale nutrient fluxes presented in Table 3.1 for the Yaquina Estuary. 



Sediment 

Advection 


Bioturbation 
Deposit Feeding 
Filter Feeding 


0 2 , DOM, 
Seston 


Neotrypaea califomiensis burrows 
Upogebia pugettensis burrows 


GED-- /I 

_ A 


Nutrients 

Seston 

Predators 


j| Tide 


Remineralization 

Particulate —» Dissolved 
Organic -» Inorganic 


Bioirrigation 


Figure A. 1 Conceptual model of the dominant processes (boxes) driving carbon and nutrient flux 
(arrows) between the benthos and water column in Pacific Northwest estuaries. 


To avoid this problem, DeWitt et al. (2004) inserted 1-m deep core barrels into sediments at 
their study sites, and fit benthic chambers to the tops of the core barrels to isolate water, sediments, 
shrimp and burrows inside the chamber from the outside world. Core barrels were inserted > 1 Od 
before the chamber tops were attached so that shrimp enclosed in the barrel could construct new 
burrow openings within the chamber. DeWitt et al. (2004) produced estuary-scale maps of benthic- 

pelagic fluxes of N0 3 + N0 2 , NH/, and DIN for Yaquina Estuary (Figures A.2 and A.3) by linking 
estuarine-scale maps of burrowing shrimp populations to density-dependent flux measurements. DIN 
fluxes had great spatial variability owing to the differences in shrimp species and abundance across the 


n 


123 







































estuarine landscape. DIN efflux (release) from sediments was estimated to be much greater than DIN 
uptake in the presence of burrowing shrimp, but in the absence of the shrimp DeWitt et al. (2004) 
estimated a net uptake of DIN by sediments. Most of the DIN efflux was projected to occur in U. 

pugettensis - dominated habitat, mostly due to enhanced NH4 efflux. Tide flats lacking burrowing 
shrimp or having low densities of N. californiensis were shown to have a net uptake of DIN, most of 

which is expected to be NO3 + NO2. DIN efflux was estimated to be much greater than DIN uptake in 
the lower portion of the estuary, mostly due to the presence of dense populations of U. pugettensis. 
Using this shrimp species- and density-dependent nutrient flux model, the benthos in the upper 
(mesohaline) regions of the Yaquina Estuary was estimated to have a large net uptake of DIN because 
of the spatial dominance of N. californiensis and scarcity of U. pugettensis. 

Much of the organic matter that is produced within, or advected into, the estuary is available for 
consumption by benthic herbivores, filter-feeders, or deposit feeders. The dominant benthic herbivores 
in Yaquina Estuary are ampithoid amphipods, isopods, and nereid polychaetes. Herbivores are not 
abundant among benthic infauna in Yaquina Estuary, with biomass 1-10% of filter-feeders and deposit 
feeders (Figure A.4). Deposit feeders are abundant throughout the estuary, and dominate the upper- 
estuary infauna. In lower and upper reaches, the most abundant deposit-feeder by biomass is N. 
californiensis. Organic matter consumption rates have not been calculated for the infaunal deposit- 
feeder guild in Yaquina Estuary. Filter-feeders are more abundant in the lower than in the upper 
estuary, primarily because that is the distribution pattern of the dominant species of this guild, U. 
pugettensis. Griffen et al. (2004) estimated that populations of the mud shrimp in the lower Yaquina 
Estuary pump the entire volume of water covering the tide flats through their burrows every day. 
Combining per-capita grazing rates for mud shrimp (Griffen et al., 2004), patterns of mud shrimp 
population distribution in the estuary (Figure A.2a), bathymetry, chlorophyll a data and a 
hydrodynamic model, it has been estimated that mud shrimp populations graze approximately 60% of 
the phytoplankton that enters the lower estuary. This estimate is similar to measured differences in 
flood- and ebb-tide chlorophyll a concentrations, suggesting that filter feeding is an important sink for 
phytoplankton in the lower estuary (see Section 7.1). 


124 




Figure A.2 Density-dependent flux of NH 4 (black triangles and dotted line) and NO 3 (gray circles 
and solid line) associated with Neotrypaea californiensis (a) and Upogebia pugettensis (b). 
Positive values = efflux from sediments, negative values = uptake by sediments. Dashed and 
dotted curves represent best-fit regression models for flux =y(shrimp burrow density). Solid 
black line represents 0 nitrogen flux. 


125 


n 














Burrowing Shrimp 
Population Density (# nr 2 ) 


Om"* 


1-30 rrv 2 

1-50 m 2 

1-40 m 2 

♦ 31-80 

51-150 

41-115 

♦81-150 

151-280 

♦ 115-220 

♦ 151-200 

♦ 281-400 

♦ 221-300 

♦ >200 

♦ >400 

♦ >300 

Upogebia 

Neotrypaea 

Mixed Spp. 



Figure A.3 a) Distribution and abundance of two species of burrowing shrimp (red = Neotrypaea 
califbrniensis, blue = Upogebia pugettensis, green = mixed species; darker color = higher 
density), b) Distribution and magnitude of benthic-pelagic flux of DIN in Yaquina Estuary 
(positive values = efflux from sediments, negative values = uptake by sediments). 


126 








Biomass , g dry wt rri 2 


1,000.0 


100.0 - 


10.0 - 


i.o - 


o.i 


□ Lower 


Herbivore 



Surface SubSurface Fitter Feeder 
Deposit Feeder Deposit Feeder 

Trophic Groups 


Facultative 

Fitter/Deposit 

Feeder 


Carnivore 


Figure A.4 Mean biomass distribution among trophic guilds in the lower, mid, and upper reaches of 
Yaquina Estuary. The error bars indicate minimum and maximum observed biomass values. 


127 


n 















































Appendix B: Description of Methods and Quality Assurance Procedures 

The quality assurance/quality control (QA/QC) program for this study is defined by the 
“Western Ecology Division Data Quality Management Plan (QMP) (US EPA, 2001). Measurements 
Data Quality Objectives (MQOs) establish the data user’s requirements for precision and accuracy. 

The Measurement Quality Objectives for each parameter in this study are presented in Table B.l. 
Quality control measures were incorporated to assure data reliability and comparability and are 
described in the QMP plan. All contributing research was performed in compliance with an approved 
Quality Assurance Project Plan (QAPP). In addition, Standard Operating Procedures (SOP’s) were 
followed to standardize routine data collection, processing and analysis for specific parameters. All 
procedural documents and QA/QC plans are approved by the WED Quality Assurance Manager. 

Standard QMP protocols include routine instrument calibrations, measures of analytical 
accuracy and precision (e.g., analysis of standard reference materials, spiked samples, and field and 
laboratory replicates), overall data, range checks on the various types of data, cross-checks between 
original data sheets (field or lab) and the various computer-entered data sets, and participation in 
intercalibration exercises. Additionally, QA/QC included ensuring field and laboratory personnel were 
properly trained and experienced. Specific QA procedures are detailed in the following sections 
relative to each data parameter. 

Accuracy and precision are indicators of MQOs and were established from considerations of 
instrument manufacturer’s specifications, scientific experience, and/or historical data. A measure of 
systematic error (measured vs. true or expected): accuracy and the random error (precision) is 
presented. Accuracy is a measure of how close measured values are to true values. In this appendix, 
accuracy is calculated using the following equation: 

Accuracy (%) = (1.0-(£(|^-F„|)/n/F,)*100 

where V t is the true or standard value, V m is the measured values, and n is the number of measured 
values. Precision is an indication of the similarity of repeated analyses or sampling. Precision is 
calculated with the following equation: 

Precision (%) = (1.0 - SD / X) * 100 

where SD is the standard deviation, and X is the mean. 


128 























03 


03 

03 

03 









3 


3 

3 

3 









C 


3 

C 

C 




■4—* 


-4-^ 

* 

>4—* 

03 


03 

03 

03 


<L> 

r— 


03 

u 


03 

O 

03 

u 

03 

u 

£ 


£ 

r“* 

c 

£ 


o 


03 

CD 

c *2 

03 

CD 

03 

CD 

03 

OD 

00 

a T3 

00 

00 

00 


u- 

o 


C/3 

c c 

O 03 

C/3 

C/3 

C/3 


3 C 

O C3 

> 


>- 


C/3 


rx 

m 

^ >n <3D 

«x 

m 

«N 

m 

•N 

m 

r\ 

ON 

^ ^ 0D 
-rt •— H 

ON 

#\ 

ON 

ON 



o 

TJ • - ^ 

o 

o 

o 

o 

o 

o 

O 


CU 


cu 

& •£ 3 

CU 

CU 

cu 

CU 

^ .> 5 

cu 

cb 

CU 


O 

C/3 


C/3 — 

Uu ra 

CU c 

« C £ 
3 3 CD 

CD TJ O. 

C/3 — 

Pu « 

CL. c 

C/3 —. 

u- 5 

C- C 

00 — 

CJU 03 

CL g 

o 

Cu 

iS CD 2 
3 3 CD 

CD "O 3- 

o 

CU 

O 

Cu 

o 

Cu 




O S 

03 £ E 

o § 

O =2 

o § 

o 

15 S £ 

O 

O 

O 




C/3 £ 

U CD £ 

00 c 

oo £ 

C/3 £ 

00 

U CD £ 

00 

C/3 

00 


c 


1 

£ 





i 

£ 



1 


d 

o 

_o 

C/3 

O 

CD 


CD 

C/3 

£ 

M 

a, 

© 

U 

o 

© 

3 

4—* 

3 

£ 

o 

o 

o 

C/3 

£ 

3 

C/3 

CU 

U 

O 

O 

CD 

£ 

3 

o 

o 

55 

> 

Vh 

CU 


o 

© 

© 

O 

o' 

o 

o 

o 

o 

o 

o 




o 





o' 





Q 













>% 

CD 

_o 

o 

CD 

W 

>> 

o 

03 

i-i 


<+* 

O tl) 
\C 3 

C> X * 1 

Uh O 

o n 

^ ° 3 

U 

o 

«/3 

CD 

C 

*3 - 

3 P. 

CD ■£ 
u 3 

£ 

oo 

^ u '£ 
U-H t- fj 

o o 

xO CD ^ 
0 s - c 

3 

C/3 

CU 

o 

U 

o 

cr> 

u, CD 

o _c 

- ’O 
U- 3 

CD £ 

£ 

oo 

£ 

m 

“ 

CD 

CD 

< 


1/3 ^ 

• 0Z 

O a> 

■+t * 

^ CD ^ 

b| & 
HH 1§ 

O 

HH 

\0 O 
© x 

o 

o 

-H 

O' 1 o 
-H £ 

*/— 

o 

nP 

o x 

o' 

-H 

3 

c O 
<N x o 

t -1 O' 

o 

o 

HH 

cd 




CD 


CN 


o 





£ 






-H 


-H 

-H 


-H -tt 




























-C 











C 


T3 

<D 










. o 


<D 

-4—* 

o 

<u 

CD 

3 

03 



P 

CD 

CJ 

B 


i 

£ 

CD 

c n 

U 

° ts 
— ^ 


o 

s- 

T3 


o 

o 

cu 

1/3 

o 

cn 

c 

CTi 

3 

CO 

00 

c 

cu 

»/o 

<N 

^ B 

CD C3 

£ 

uo 

cd 

<D 


i "y 

m 

<N 

(N 


U 

cn 

£ “ 

r—» 

S3 

■4—# 

O 


o 

1 

' 

1 

o 

O 

1 

o 

-4—* 

\° 

o ON 

1 

o 

03 

X3 

S-i 

.2 

o. 

X 

w 

cn 

o 

■o 

S 

c 

C/3 


o 

O 

O 

• 

1 

o 

o 

O 

rxi o 

O <N 

1 

o 


m 












cd 

_> 


s- 

CD 









c 


■ 4 — * 

o 











H .2 


CD 


£ 

ca 

U. 

03 

a 

• mm 

1 

£ 

CD 

C/3 

£ 




m 

1 __ 



o 

m 

X>‘ 

O 

-4—* 

units 

psu 

U 

o 

ntu 

meter 

mS err 

psu 

O 

o 

mg r 1 1 

Satura 

u- 

(D 

<D 

£ 

"s 


s 









nO 

o x 


<5 

- 4 — » 


s 












M 











C 


C/3 











CD 

£ 


> 











<D 


■a 











u. 


e 









3 


W3 

03 

u. 

CD 
•*—> 

03 

Q 

H 

U 

Q 

Q 

Q 

Q 

Q 




CD 

CD 

>x 

CD 

<D 

2 

CD 

£ 

03 

i- 

.^U 
> td 

H 

U 

T3 

£ U 
5 *o 

H 

U 

^ T3 

H 

U 

"O 

Br* cd 
■> -o 

.3 C 

CD 

X3 

C 

£ CD 

1 c 

X 

O u 

, , o 

c 

Td 

C 

o 

C/3 

Table B.l 

cz 

Qh 

"O 

— 

£ 

a: 

CD 

cn 

© 

§ op 
1 $ 

Salinity 

Sea-Bin 

03 £- 

cd CQ 
Cu 1 

c 

3 CD 
r <lD C/3 

H 

Turbidit) 

Sea-Bir 

Depth 

Sea-Bir 

o ° 

3 W 

T3 — 

c 

O >H 

U 

Salinity 

YSI So 

2 o 
S oo 
cu ^ 

£ C^ 
CD >- 

H 

CD o 
^ C/3 

O ^ 

•S3 >. 
Q 

Depth 
on YSI 


n 




























s- 


in 

in 

in 

in 

IT) 











<D 


o 

o 

o 

o 

o 











— 


o 

o 

o 

o 

o 

IT) 

m 









o 


<N 

fN 

CM 

(N 

<N 

o 

o 


> 










C\ 


c\ 


o 

o 


<D 







o 


CQ 

CQ 

CQ 

CQ 

CQ 

fN 

fN 











GO 

GO 

GO 

GO 

GO 

cv 

#N 

fN 

CQ 

tT 

\Q 







C/5 

cu 

O 


u 

D 

-j 

U 

D 

-J 

u 

D 

-J 

U 

D 

-j 

O 

o 

-J 

fO 

< 

Tf 

rn 

ro 

< 

T" 

co 

o 

Cu 

O 

GO 

GO 


o 

<N 

O 

o 

ri 

o 

O 

fN 

O 

o 

fN 

O 


GO 


< 

< 

< 

< 

< 

GO 

c/i 

C/1 


Cu 

CU 

Cu 

Cu 

Cu 

Cu 

CU 

CU 




c/i 

GO 

GO 

GO 

GO 

CC 

Cd 

at 

DO 


< 

< 

< 

< 




2 


2 

2 


£ 

£ 

£ 

S 


a 

a 

a 

a 

d 

r~| 

o 

c/3 

’o 


5% 

5% 

5% 

5% 

£ 

o 

£ 

in 

£ 

in 

in 

in 


T £ 
tO CD 
- £ 

t—4 

CD —4 

§ 

sO 

0 s 

uo 

£ 

o 

VI 

_o 

on 

'> 

<D 

S- 

cu 



-H 

-H 


-H 


o S 

r" fN 

on g 

ON 

> 

O 

Q 

















>, 

















tO 













CD 




O 

















o 

o 

o 






vO 




vO 


sc. 

£ 

fN 


G- _ 

O "g 

w 

c 

c 

<d 

03 

S3 

o 

o 


±5% 

s© 

o N 

in 

-H 

±5% 

±5% 

O 

n 

±5% 

±5% 

15% 

0 s 

in 

5 


03 

C/3 

‘to 

± 2 m 

N® 

O' 

m 

ON 

v o 03 
o' T3 

O S3 

— 03 

4— » 

4—* 

C/5 

< 












- 


-H w 

<D 













• 




£ 













O 






































DC 

















T3 

CD 












fN 


1 

TD 


<D 

o 

bJJ 

c 












£ 

fN 

i 

cs 

fN 

£ 

£ 

T3 

bO 

sp 

jd 

o 

o 

03 

-4—> 

03 

U. 

T3 

U 

•4—> 

g> 

CD 


o 

o 

1 

o 

1 

o 

m 

i 

o 

m 

i 

o 

1 - 100 

o 

o 

i 

o 

0-3 

1 - 150 

0-200 


TS 

bD 

o 

o 

c 

o 

o 

o 

»s 

in 

0 s 

O 

uo 

1 

c3 

T3 

a. 

X 












ro 

1 

in 

o 

o 

S-H 

DO 












O 


4—* 


«2 

C/5 

<D 















O 


















> 
















C/5 

4— > 














fN 

'c 

— 

C3 

o 

<L> 













(N 

i 

£ 

T3 

<D 

Db' 

O 

C/5 

c 


1 

!_ 

2 

!_ 


2 

=L 

i 

to 

i 

to 


C 

C/5 

-4—* 

o 

fN 

i 

£ 

>—D 

C/5 

^ nO 

p*~~> 

15 

r-3 









E 

zL 


TS 

to 

o 

U5 

£ 

T3 

b0 

bO 

U 

















to 

c/ 


^5 
















c 















c 


o 








C/5 







<d 

















E 

1) 

s-> 

=3 


Ui 

s 

Z 





Tf 


C/5 

'o 

GO 


C3 

03 

c 



CD- 

03 


c/3 

03 

<D 

2 

02 

<D 

dd 

Parametei 

s 

£ 

_5 

"c 

U 

s» 

o> 

■u- 

« 

1 

m 

O 

Z 

T3 

CD 

J> 

o 

C/3 

C/3 

1 

fN 

o 

z 

TD 

CD 

_> 

O 

C/3 

C/3 

+ 

tj- 

I 

z 

T3 

<D 

g> 

o 

C/3 

C/3 

i 

m 

O 

ci¬ 

ts 

CD 

_> 

O 

1/3 

C/1 

X 

o 

CZ1 

TS 

<D 

’o 

C/3 

C/3 

sc 

CD 

bD 

O 

v- 

•4—4 

z 

■4—4 

o 

!— i 

O 

DC 

CD 

C/3 

O 

. <—1 

CU 

13 

-4—* 

TS 

(D 

T3 

SC 

CD 

CD 

C/3 

GO 

ii 

C 

£ Q 

O g* 

^ D. 
>- O 
<D V- 

« ° 

53 

JU 

53 

5 

5 

6 

C/3 

C/5 

C/5 

C3 

£ 

o 

4—> 

s 

e 

(U 

Q 

-4—* 

O 

o 

<D 

G- 

O 

<D 

•4—4 

03 

u- 

4— 1 

t; vi 

o *2 

Z 

X 

o 

CD 

C/5 

03 

H 


£ 

5 

Q 

5 

s 

5 

o 

H 

o 

H 

o 

H 

^ -s 


s 

DC 

GO 

U- 03 

a 3 

C/5 

H 




































Table B.l Measurement Quality Objectives for data collected by Western Ecology Division. 


1— 













D 













r 













o 


























o 










o 





i < 

. < 



■ 

. 



• 


' ' CN 

c n 

o 

© 

o 

p 


© 

© 



o 

o 

O 

5 o 

cu 

O 

CN 

O 

o 

o 

o 


"T 

o 

oo 

ON 



o 

CN 

cn 

o 

S 5 

GO 

— 

cu 

cu 

c_ 


cu 

CU 



cu 

cu 

Cu CU 


Cu 

cu 

Cu 

cu 


c_ 

cu 



cu 

Cu 

CU CU 


< 

< 

< 

< 


< 

< 



< 

< 

< < 


a 

a 

a 

a 


a 

a 



a 

a 

O' O' 


nO 












r* 




60 


60 





_ D 


o 

o 

c 

E 

c 


C 





o 

o 

d 

•— 

cu 

VI 

> 

U 

£ 

-H 

£ 

-H 

c 

o’ 

-H 

n/a 

© 

-H 

10 % 



0.70 

o.lg 

sampl 

0.5 % 












D 


>> 

o 

CS 

<UH 

o 'g 

\0 CS 
o'- "O 

£ 

£ 

£ 

£ 

no 

o N 

£ 

o 

N© 

o x 

N® 

c x 



N® 

o x 

in 

cu 

£ 

cS 

C/2 

o 

so 

c\ 

o 

o 

< 

o c 

— CS 

-H “ 

CN 

-H 

-H 

-H 

o 

no 

VI 

-H 

o 



-H 

‘eo 

m 

© 












© 


D 











<N 


60 

c 

£ 

c 


60 

£ 

60 





£ 

m 

(N 

cs 

£ 

£ 

£ 


£ 




N® 

£ 

'O 

o 

o 

£ 

o 

►J 

o 

O 



o 

T3 

+ 

D 


o 


o 


o 

o 



o 

60 

o 

u- 

o 

D 

— 

CN 


in 

o 

in 

1 

o 



1 

o 

O 

o 

CN 

CU 

© 

1 


© 

1 

o 





m 

1 

X 


m 



o 








OJ 








• 



o 



CD 








<N 




P 

C/3 






cS 



£ 

<N 

i 

E 


C/3 

C/3 



mg 


mg 

CD 





u—* 

*E 

3 

>4—* 

.60 

a; 

mir 

mir 

cm 

u* 

03 

s® 

c x 



c 

CD 

O 

i- 

<D 

£ 

T3 

60 

%0 


60 









Cu 





















-4-* 





13 


c 






| 

TD 

C/3 

P 

C/3 


S— < 

O 

P 

C/3 


D ^ 

60 Z 

Parameter 

cu 

d 

C3 

uc 
+—> 

60 

C 

D 

O 

r*; 

& 

'*—* 
o 

% 

Q 
-*—* 
o 

t-H 

CD 

£ 

_o 

Q 

K 
* »«■* 
u. 

^3 UC 

C/3 

03 

£ 

_o 

s 

CD 

>> 

uc 

Cl 

> 

s~ 

(D 

> 

o 

U 

+-> 

c 

cs 

(D 

CS 

CS) 

13 

o 

u 

(J 

CD 

CS 

jsO 

CS 

<u- 

O 

Uh 

D 

> 

O 

> 

CD 

u-> 

CS 

H 

u-> 

C/2 

GO Is 
CD £ 

C/3 

C/3 

03 

£ 

O in 

u- — 

.tC 

C w 

« .2 

CS uu 

«) g 

n « 

2 & 

D O 


C/3 

C/3 

H 

o 

uc 

cn 

o 

uc 

c/3 

o 

_ c-; 

00 

- a 

. • <l> 
N T3 

Epi] 

wt. 

Cu 

N® 

CS 

s 

O 

v? 

o x 

1— • — 

« on 
CO CD 

_o 

s 

JS o 

S .2 


Burrowing Shrimp Parameters 








































CL 


•— 










X 


<u 












r- 










G 


o 

V-H 









L—* 

(J 

G 

5/3 G 

>-» c 


o 

O 

o 

o 

o 

o 




G 



o 

o 

<b 

o' 

o 


— 


J— r , 

’M 

5/3 G 



o 

o 

o 

o 

o 


o 


C Oh 

C/3 S 


C_ 

o 

o 

o 

o 

o 


CL 


O GO 

CL ^ 


O 

C/3 

04 

CL 

ON 

Cl, 

rj 

Cl 

ON 

CL 

<N 

CL 


o 


° C 
>. G 

a 3 



CL 

Cl 

CL 

Oh 

CL 


Cl 


o o 

C/3 



< 

< 

< 

< 

< 


o 


S G- 



a 

a 

a 

a 

c/ 


C/3 


S Q 

u a 


c 




<N 

r<3 





r- 


'O 

m 

o 

r- 

o 

r-- 

£ 

o 

£ 

o 

£ 

o 


N® 

©n 


< 

c 

U-3 

c 

o 

o 

0 ) 

© 

© 

-H 

-H 

— 


-H 



— 

~CO 

• 

Cu 



-H 

-H 




4 

> 












n 




































GO 












o 











c 

o 

o 

w 

>3 

o 

C3 

N? 

o x 

N? 

o x 

NO 

o x 

<N 

£ 

m 

£ 





G 

in 

J-H 

3 

o 

o 

o 

o 

o 


IT) 


o 

ri 

£ 

O 


“ 


in 

m 


-H 


41 

»n 

<D 

CO 

<L> 

£ 

o 

< 

-H 

-H 

-H 

-H 

HH 



o 

41 














-O 

-a 

<D 










o 

in 

0) 
-*—> 
o 

JD 

GO 

G 

C3 

S-H 

(N 

i 

£ 

<N 

i 

£ 

(N 

i 

£ 

<N 

£ 

o 

m 

£ 

co 


o 

in 


o 

+ ^ 

<N ^ 

oo lJ 

o 

o 

-2 

-a 

T3 

<U 

o 

<u 

a. 

X 

o 

o 

in 

1 

o 

o 

o 

VO 

1 

o 

o 

o 

o 

in 

1 

o 

o 

o 

VO 

«A 

o 

o 

oo 

1 


in 

cb 


o H 

° s 

1 

O ^ 

2? $ 

NJ fT) 

u, 

a 

LU 










=? ? 











<L> 












> 
-*—» 



Os| 

1 

£ 

(N 

i 

g 







CO 

S- ^ feX) 

O 


C4 








<L» GO r- 

4) 



CO 

in 







2 .£ 

G -G ^ 

-O' 

CO 

£ 

03 

03 

<N 

m 


60 

£ 



o 

>> 

"2 

G 

0/3 

<u 

o 

G 

T3 

> 

G 

T3 

> 

err 

cm 



GC 

♦— co * 

• • « o 

2 “ Z 

G 

G 


LG 

-5 

_c 

*5 

_c 








a 












4—* 


— 










G 










1 

<u 


O 



03 

O 






CL 

£ 


X 


<u 






X 

<L> 

V- 

G 

C/3 

03 

<D 

*-i 

<U 

■*—> 

<L> 

£ 

03 

V- 

a. 

£ 

c 

_G 

C/3 

GO 

CL 

£ 

c 

-C 

C/3 

GO 

03 

i— 

-O 

<D 

•*-> 

G 

<u 

> <u 
c o 

S-h 

< 

<D 

O 

.G 

4— 

S- 

G 

<D 

£ 

p 

o 

> 

G 

G 

-J 


5/3 

0 / 

s- 

G 

G 

L- 

b 

£ 

03 

£ 

G <U 
.2 tn 

*S «" 

O c/3 


03 

G 

C 

^ c 

C/3 



_ 

-O 

CL a 

cd 

0-4 

£ £* 

b ^ 

o 45 

£ 

£ 

G 

u 


G 

L> 

b 

1 03 

o t M 

X) 


c c 

S '2 

C c 

2 c 
c 5 

O 

fc 

o 

G 

<u 

c 

CO 

CO 

</> 

G 

c3 


G <U 

G <U 

<d -O 

G 

G 

o 

03 


CO 

b ^ CL 

H 


CQ Q 

CQ Q 

QQ < 

CQ 

CQ 

a 


Cl 

w 

a u a 































C/5 

i- 

-—' 
<u 

£ 

p 

•— 

P 

Du 

X 

P 

03 

C/2 

c 

<u 

£ 

g 

C/3 

e 


2 

C/2 

X 

o 

<u 

r" 

u 

o 

•— 

p 

o 

CJ 

X 

c 

P 

P 

_o 

03 

•— 

X 

’p 

U 


c3 

P 

a 

(N 

CQ 

X 

03 

H 


<u 

C3 

Vi 

4> 

2 u 
03 *33 

> 2 
X S 
•- cu| 

r- D 

o <-> 

• is o 

51 


<D 

O 

c 

C3 

a.g 

8 & 
o •- 

< o 


o 

r- 

c 

=5 

cr 

4> 


X 

o 

<L) 

x 

U 

o 

j— 

c 

o 

U 

a 


<u 

H 

=3 

X 

4) 

O 

o 

lx 

O. 

c 

o 

•!—> 

03 

lx 

X 

"p 

U 


c 

<D 

£ 

g 

C/2 

p 


<u 

00 

03 

X 

o 

03 

O. 

ON 


w 

CQ 

on 


£ 

p 

0) 

}-H 

b 

O 

4—> 

o 

03 

U. 


o 

a 


X 2 
it: <o 
X &| 

X c /2 
4- ^ 

xx C/2 


4—» 

’> 

4—* 

o 

p 

X 

P 

o 

o 

p 

£ 

o 

p 

-id 

4-i 

O 

P 

o 


o 

00 


b 

o 


o 

.03 P 

^ o 

X X 
*- c3 

CQ x 

03 X 
<U c3 
00 4> 


b 

4 —< 

o 

p 

X 

C 

o 

U 


4> 

lx 

b 

o 

•4—< 

o 

03 

Ux 


O 

a 


.22 15 

X o 
it: <u 
X CM 

X C/2 
4- P 

xx C/2 


E- 

oo 


% s 


j§ g 

lx p 

2 fe 

03 -P 
^ 4> 


4) 

o 

03 


X 

03 

a> 

o 

03 


b 

o 

4—» 

o 

*2 § 

X X 
*- 03 

X I- 

0Q X) 

03 x 
<D c3 
C/0 o 


4> 

s- 

B 

p 

ix 

<u 

a. 

£ 

<u 

H 


pi 

4> 

b 

o 

-4—> 

o 

03 

U- 


O 

a 


.22 'S 

•4—» 

it: <u 

Pi a - 

~ C/3 

4^ 3 

—h C/2 


t: . 

O 03 

Ljl, <U 

P ^ 


T3 

1) 

'— 

03 


C3 

z 

p-tr 

£ <l> 

O "S 

° £ 

op § 

P s- 

w oo 

5-1 X 

•S (D 
~ ^ 
£ b 


<u 

Q- 

4-* 

<L) 

OO 


b 

o 

4-* 

o 

<2 § 

X3 X 
^3 03 

'X *- 

CQ X 

03 X 
4) c3 
OO O 


4) 

s- 

P 

0/3 

C/2 

D 

•— 

o- 


D- 

<u 

Q 


<o 

u, 

b 

o 

CJ 

03 

X 


o 

a 


"e3 

P 

P 

P 

03 

CQ 


'— 

P 

C/2 

O 

D- 

X 

<u 

X 

C/2 

t- 

03 

4) 

O 

P 

O 

o 

p 

t-4 

03 

O 

OO 


(N 

b 

(O 

b « 

2 p 

cS -2 

o X - 

U x 5 

X C3 W 

X O >4 


0/3 


C/3 

U< 

O 

C/2 

P 

<L> 

C/2 

< 

D- 


C/3 

oj 

■4—1 

I 

4) 

•— 

X 

p 

03 

P 

P 

-£ 

U 


ON 

b 

A 

4 


o 

xO 

0 s 

WO 


Cdi <N 

u. "H 


p 

<u 


<D P 

a. x 

■§:§ 


o 

■b -b 

s i ^ 

P 03 X 

C 3 c 

cs cr 5 

— ri £ 


C/2 

> 

C/2 

X 

C/2 *Q 

g s 

° .2 x 
M2 b <o 
<u x x 
> ° c 

O c/3 4) 

O- 

C/3 

P 

C/3 


i 4) 

CO - 

b'-E 

O P P 
P o- o 

p 


o X 00 
— p 


4) 


O 
X 
p 

J | E t 

• O- 

— 1 c /2 (N 


4» 


b 

o 

o 

,p 


p 

o 


'P 

O u> 

a. x 

p x 
<u C3 
OO O 


■4—* 

X 

X 

•— 

p 


o 

a. 

p 

4) 

OO 


4) 

s- 

X 

P 

P 


4) 

l— 

o 

o 

p 


b 

o 


C w 
P X 

« X o 

U ”~" C3 cc3 
O 4P 



P X 


O P 


ZZ ^3 


p C/2 

«>. cj x 
b^= o 

o 

o •£ 

o 

« 4) 

M3 

Q- c 

c4) C 


C/3 

X 

j- 

P 

X 

p 


C/3 


o C3 

•C g 

r" c j- 

b « 0/ 

4) P »-H 
P g C 

X ° o 

X - Q, 
c 4) ^ 

c X X 

£ O g 

i—4 4 -* C3 


«3 C3 

p ^ 
• P 4) 

p -r 

oo b 

P 4) 
«- 
X 
4> 

X 
OJ 
4) 

•g WQ 
O X3 
i/3 

u P 
4) X 

ts £ 

£ w 
p 


p 

o 

Cl 

P 


P 
4) 

£ 

X 

p b^ 

^2 £ 
CD O O 

<! M3 b 


C/5 


c2 

X 

4) 

■4—> 

P 


C/3 

P 

_o 

p 

s- 


C/3 

•— 

4) 

•— 


C/2 

P 

O 


X X 

p x x P 

O P 4) •§ CCS 

b “ i <s ^ 

ox S p -t: 

•4—> -4-4 X P 4) 

O a- 4 § 4) 
P4)4/cO- 
X X Cl £ c« 


4) 

4) 


P 

H 

a. 00 

.S - ^ 

4-» ’733 

2 b O 

>-! 2 OO 


o 

o 

X 

X 

X 

G ^ 
P c« 

o "a/ 
o x 
o o 

S £ 


c 

p 

1 4» 

4) 5— 

V- s- 

X ^ 

C 4) 

2 « 

P x 
4) X 

n p •» 

X> 4> X 


^2 

— 

o 

o 

p 


C/3 

c 

p 

00 

P 

X 

4> 

X 

4) 

4) 

OJ -q 

c/3 

v- P 
4) X 

O £ 
£ -2 
P w 

fe b 

X o 

4-* 

— o 

< M3 


4> 

X 

i 

X 

p 

p „ 

M C/3 

E s 

O0 4> 

> £ 


C/3 

4) 

_P 

"p 

> 

oo 00 
O P 

o*-a 


o 

4) 


C/3 4-> 

d) C/3 
O 4) 

o tr 

4) 


U 5. £ 


O 

a 


p 

p 

c 

p 

< 


4) 

■4—> 

’n 

4) 

4) 

P 

4) 

s— 

o2 C/3 

4) 00 

i- c 

x 
O p 
oo £ 
D 


4) 


4) X 

0-3 

b £ 


C/3 


> £ 


o 

X 

4) 

4) 

a. 

C/3 


C/3 
4) 

P 
lx 

X 

"p 
4) 

. i — 

b <u 

4) 

OO 


co 

CO 


C/3 

4> 


P 

C/3 


p 


p 


•— 


X 


x 


p 


4) 


4) 


X 

4) 

-4—1 

C/2 

C/2 

p 

p 


-4—> 

b 

X 

4) 

00 

> 

X 

4/ 


OO 

X 

a 

Q 

l 

H 

S 

U 


it- 

























o 

4—* 

O 

a 

o 

4—> 

3 

L- 

3 

07 

DC 


O 

a 


"s 

3 

3 

3 

3 

s 


07 

Lh 

on 

O 

cl 

X 

<u 

C/3 

5— 

3 

_07 

CJ 

3 

O 

O 

3 

j— 

3 

O 

CO 


on 

b 

07 

. > 

O I 
O 3 
£ .2 

S £ 

cj £ 


•-5 3 1) 

i—1 07 


< 

GO 

co 

ON 


l- 

O 

cj 

• 


o 

C/3 

3 

07 

C/3 


07 

07 

3 

3 07 


3 

-O 


o g 

3 hh 

•4—< #-* 

3 
O 

O 


o 

3 — 

3 07 


3 


3 

3 

O 

C/3 

!— 

<u 

Cl 


07 

07 

c c 

I £ 

.3 07 


X c/3 
hh C/3 

'£•§ 


07 

3 

07 

07 

L- 

'-2 

07 

ca 


3 

_o 

'c/3 

C/3 

07 

C/3 


C/3 

-<—» 

C/3 

3 

3 c/3 

<u 'S3 

> 3 

X 2 -O 

3 __ 
3 


^ C 
C* <U 
o 1/2 
2 07 


o 

3 

Uh 


CL 

07 

La 


3 

3 

O 


07 <U 
o 07 
3 J2 
3 cl 

3 • 

CQ Q 


o 

a 


07 

C/3 

3 


07 

3 

07 

07 


07 

CQ 


O 

to 

.3 

*6 

3 

07 

Lh 

X 

o 

07 

-3 

CJ 


3 

07 

> 

O 

tO 
' 3 


i 

07 

Lh 

"O 

3 

3 

07 c/3 

3 H^ 
O- 

•§ £ 
3 3 

C7 ^ 

I 

<u £ 


to 


to _ 

.2 ^ § 

.£ ^ *3 M 3 

3 O +1 > 

07 T3 u >. 07 
on C "O ^ 3 

x: § £ c* B 

.£P « *2 J3 £ 

r- c/3 3 ;•+- 3 

" o_ 3 't- o 

c o m id c 


07 CT* 
X 07 

C2 ^ 

T3 a) 


2 s 

“ -a 

^ c 
<C 3 


3 

2 


3 


3 


C/3 

GO 

3 


W3 67 
on 




3 t0 

O T3 J= 
07 3 ^ 

D (5 l 
w . 3 

-a % « 

— o £ 

O w p 

C/3 


X 

o 


GO 43 

<— C/3 

~ = .£ 
07 T3 
<—I 3 Lh 

u S <S 


3 

3 -a 
O 3 
•-3 O 
3 O 

Lh aj 
X C/3 

^ :2 
° ’o -o 
3 ™ 3 

•3 GO -a 

a.s g 

I (73 i2 
(N 3 c/3 


O 

o 

E 

o 

Lh 

o 

_3 

Uh 


O 

< 

i 

o 

Lh 

o 


3 

3 

CL 

60 


<D 


<U 

> 

3 


3 SZ 


<U 

H-H 

3 


_C 0 CL 

‘5 LC 
S “ _5 

g s i 

<3 —. cd 


"O 

<u 

o 


o 'O 


o 


r \ 3 QJ 07 

3 Lh c/3 


60 

£ 

<N 

-HI 


o 

3 

<u 

07 

Lh 

,o 


3 

_o 

M 

07 00 

^ 07 

C/3 


CQ 


C/3 

07 

C/3 

C/3 

3 

3 <17 

c to 

- 3 

CO 3 

h Lh 

C/3 "O 

52 07 

3 -i—> 

O g 


CL 

X 

07 

Lh 

07 


I/O 

G-h 

o 

c/3 
60 > 
.£ o 

g « 


3 

_o 

H—> 

3 

Lh 

X) 


C/3 


60 

'S 


3 


07 


3 
07 

^ .2 

g -3 

^ .ts 

u ^ 


07 

07 

3 

j3 

3 

JD 

O 

Lh 

07 

s 


07 

07 


07 £ 

.2 07 07 

07 c/3 


07 

3 


5p 2 60 ^ 

2 “ « e3 

H 'O X6 (73 

<— 1 o c/3 

t« § > g 
.2. T3 X 67 
"O' u 60 c 

< *x: ^ he 


07 

3 

07 

Lh 

£ 


C/3 

07 


-o 

07 

Lh 

3 


C/3 C/3 

c/3 3 

3 07 

„ £ £ 
07 07 tO 

c£ c 

r 2 07 

U O .3 


07 

3 

07 

07 


C/3 

c75 


f O JC 
« 2 
CQ 3 


60 

C 

•c 

07 

07 

07 

3 

07 

C- 


Lh 

07 

•*—> 

07 


C/3 
(73 

3 

S GO 


O 

07 

CL 


3 

_o 

4—» 

C/3 

X 3 

£ .2 

O £ 

07 « 

a 07 

3 L- 
CL -rt 
07 2 

Csi 3 


07 >N 
N j= 

JX* (73 

C/3 

07 
07 
07 


3 

3 

3 

07 


- 0*h 
C/3 *FH 

3 07 

£ .2 

-I 2 

O 07 
07 (73 


60 

3 

O 


o 

''P 

as 

<N 

O 

-HI 


"O 

07 

L- 

3 

C/3 

3 

07 


07 


07 

3 

3 

> 




07 

> (73 

07 07 

07 CL 
3 £ 

o S 


60 

3 

Lh 

o 

<+- 

o 

_o 

H—> 

3 

Lh 

07 ^3 
O. LJ 

o 3 
-a 
o c 

.2 3 

H-H 

3 172 
07 o 
60 *S 

o £ 

Lh 3 

.tt 60 

H— r J—< 

Z o 


C/3 

C/3 

3 


07 

H—> 

07 

£ 

o 

L. 

-HH 

07 

07 

CL 

GO 


Lh Ct 

^ « g. 

o o. £ 
4= o : 

hh H (73 
>—' « M 
07 3 07 

07 07 c 

£ ^ 
3 — 

07 tO 3 
<— . zr in 

CJ "3 *07 


X 

07 



07 

(73 


07 

3 

07 


C/3 

C/3 

3 

07 

CO 


07 

3 

L. 

3 

07 

O 

3 (73 

^ £ 
g .£P 

X 07 

U J 


>s 

x 

3 Z 4 

Ot -H ^ 
„ i—i C/3 

HH 3 07 
3 3 07 

Xg > 

S o CO 

«le 


3 

3 


3 C 


> O ,9 r 

< o CJ cc 


o 

n 

H 

< 

L- 07 
07 07 

, '4—* G 
-2 
3 

Z X) 


CO 

















Water Quality CTD Profiles and Grab Samples 

Profiles of water quality parameters were measured using the Seabird SBE 19 CTD with 
data logging capability. Variables measured by the CTD and other associated instrumentation 
are depth, temperature, conductivity, turbidity, photosynthetically active radiation (PAR), and in 
situ fluorescence, and calculated variables include salinity and density. During 2006 cruises, 
dissolved oxygen was measured at discrete depths. Discrete water samples were collected at 
representative depths (surface, mid-depth, and bottom) for analysis of chlorophyll a , total 
suspended solids, and dissolved inorganic nutrients. Since this report includes data collected for 
various projects and principal investigators, the number and location of discrete samples varied 
with sampling interval. 

For each water quality profile, the CTD was lowered to the bottom, and discrete water 
samples were collected during the upcast at bottom, mid-depth and surface depths. Near-bottom 
conditions were measured at 0.5 m above the bottom. Data were collected every second and 
binned with ‘Seasoff software into 0.25 m discrete intervals. During 2006 cruises, dissolved 
oxygen was collected using a YSI 6600 Sonde attached to the CTD cage. The YSI sonde was 
calibrated prior to use following the manufacturer’s specifications. Light attenuation coefficients 
(, k ) were calculated for the water column on the downcast. Prior to analysis the data were 
reviewed to eliminate any false reading caused by reflection from the aluminum boat. Care was 
taken so that the PAR sensor on the CTD was not in the shadow of the boat. 

The water column was sampled at each site for dissolved inorganic nutrients (Si(OH) 4 , 

NO 3 +N0 2 , NH 4 , and P0 4 3 ), total nitrogen and phosphorous (2006 cruises only), chlorophyll a 
concentration, and total suspended solids (TSS). Water column samples were collected and 
prepared per MES SOP09.rev 0 (2003). Water quality samples were filtered and processed on 
board the boat or upon return to the laboratory within several hours of collection. 

A performance-based approach was used for evaluating the quality of the chemical 
analysis. Depending upon the compound, laboratory practices included 1) continuous laboratory 
evaluation through the use of Certified Reference Materials (CRMs) and/or Laboratory Control 
Materials (LCMs), 2) laboratory spiked sample matrices, 3) laboratory reagent blanks, 4) 
calibration standards, and 5) laboratory and field replicates. 


n 


135 


Chlorophyll a and Total Suspended Solids 

Water samples for chlorophyll # and total suspended solids (TSS) analyses were collected 
in duplicate and filtered on board (if possible) or upon return to the laboratory. Typically, the 
samples were filtered within 1-2 hours of sample collection. For TSS analysis, 1-liter of 
unfiltered seawater was collected at relative depths as described above, filtered on board the boat 
and further processed according to SOP WRS 14B.2. The complete procedure for sample 
processing and analysis of chlorophyll-# samples is detailed in WED SOP06.rev 0. Standard 
Operating Procedure for Preparation and Analysis of Estuarine Water Samples for Determination 
of Chlorophyll-# content. The samples were stored in the freezer until analyses. Chlorophyll # 
was extracted by sonicating the filters in 90% acetone and quantified using a fluorometer (10 AU 
Fluorometer, Turner Designs, Inc, Sunnyvale, CA). The fluorometer was calibrated with a 10- 
AU solid secondary standard and “blanked” with freshly prepared 90% acetone solution prior to 
each sample set analyzed. The high setting of the solid secondary standard was used in 
calibration and the low setting was used as a quality control check after calibration. During 
analyses, the solid secondary standard and 90% acetone blank were checked midway through 
and at the end of a sample set to verify that the fluorometer performance had not changed. If the 
solid secondary standard high setting differed from true values by ±1%, the instrument was re¬ 
calibrated and the previous half-set of samples were reanalyzed. 

The solid secondary standard was calibrated to chlorophyll # concentrations using fresh 
chlorophyll # standards provided by the manufacturer (Turner Designs). The fluorometric 
chlorophyll # standards supplied by the manufacturer (Turner Designs) consisted of a high 
concentration (181 jug l' 1 ) and a low concentration (18.2 pg F 1 ). The solid secondary standard 
was calibrated with newly purchased standards in 2002. During 2006, the solid secondary 
standard was checked using additional set of chlorophyll # standards. This quality control check 
revealed that the accuracy was 99.2%. 

To assess the accuracy of the chlorophyll # measurements, we compared the known value 
of the solid chlorophyll # standard (low setting) to the actual measured values of the solid 
standard. Accuracy analysis was performed for all chlorophyll # presented in this report. The 
accuracy and precision for the chlorophyll # data reported in this study are estimated to be 
98.7%. 


136 


Nutrient Data 

Nutrient analysis for nitrate+nitrite, nitrite, ammonium, phosphate and silicate was 
performed by the Marine Science Institute Analytical Laboratory (MSIAL) of University of 
California at Santa Barbara. Nutrient analysis was carried out with a Lachat Instruments Model 
QuickChem 8000 Flow Injection Analyzer. Data quality indicators include representative 
calibration data, reagent blanks, replicate analysis and percent recovery analyses of spiked and 
control samples. Acceptable levels for these parameters are detailed in the MSIAL QA 
guidelines and provide means of monitoring data quality (MSIAL Quality Assurance Manual 
2005). In addition to these internal QA checks samples obtained from the National Institute of 
Sampling and Technology and other producers of certified reference material are analyzed 
periodically to audit performance. Deionized water blanks and sea water blank (low-nutrient 
natural sea water, aged to allow nutrient values to drop to near-zero levels) are also run. An 
independently-prepared “control” solution containing an intermediate concentration of each of 
the nutrients is also prepared. The chemistries used in determining the various nutrient species 
on this instrument have been developed by the manufacturer to have little or no salt effect, so the 
analytical response is the same for fresh, DI water samples, and standards as for salt water 
samples and standards. Saltwater samples, however, exhibit a refractive index-related response 
in the flow-through detector, so the sea water blank is used to adjust the measurement timing 
parameters to compensate for the refractive index effect. Instrument calibration is checked at the 
beginning of a sample-batch run, at the end of the run, and periodically during the run. Each 
calibration sample is analyzed in duplicate, and the resulting data is used to establish calibration 
curves for each nutrient species. If the mean of the two replicates of any standard differs from 
the known concentration of that standard by more than ten percent or more than one-half the 
concentration of the lowest standard, whichever, is greater, the calibration for that species is 
considered invalid and the calibration run is repeated. (See MSIAL Quality Assurance Manual 
2005 for more details) 


137 


' i- 


Table B.3 Precision and accuracy (expressed as %) of phosphate, silicate, nitrate+nitrite, 

ammonium, and nitrite Data. 

Measure 

P0 4 ' 

Si(OH) 4 

NO 3 +NO 2 " 

nh 4 + 

N0 2 ‘ 

Precision, % 

97.6 

(n=265) 

98.3 

(n=258) 

98.2 

(n=266) 

98.0 

(n=265) 

94.6 

(n=7) 

Accuracy, % 

98.2 

(n=82) 

98.2 

(n=253) 

96.5 

(n=195) 

96.8 

(n=140) 

97.9 

(n=4) 


Total Phosphorous and Total Nitrogen 

Total nitrogen and total phosphorous analysis was performed by the Willamette Research 
Station (WRS), an EPA (WED) research facility in Corvallis Oregon. A Lachat Quikchenv 
8000 Two-Channel FIA was used for analysis. WRS adheres to strict EPA QA/QC procedures 
and the following procedures are detailed in the standard operating procedure document WRS 
34A.3 (2005). 

A second source check standard (SSCS) (NIST traceable) was included in each 
automated analyses run. Instrument calibration and stability was validated after every tenth 
samples. A blank (Reagent water) was run after each SSCS to ensure negligible carryover. 
Calibration verification was monitored throughout the run by checking SSCS recovery. If 
measurement exceeded ±10% of the theoretical value, the instrument was recalibrated. A control 
check standard (QCCS) is a bulk sample digested at least once each digest batch set to show 
inter-run consistency. The bulk sample is collected as needed from a local stream or river and 
prepared in the same manner as samples. An analytical duplicate was run as a separate analysis 
(digestion and analysis) no less than once every 10 samples. Inter-run consistency and column 
performance was monitored with the QCCS (bulk sample) that is analyzed once each analytical 
run. 

Three of each of the TN and TP quality control check standards (nicotinic acid and 
ascorbic acid) were digested with each batch to verify TN and TP recovery. Three reagent water 
blanks were digested to determine the nitrogen and orthophosphate blank present in the mixed 
persulfate digestion reagent. Digested standards and blanks were used to monitor digestion 
efficiency and background contribution of the persulfate. A method detection limit is the 
minimum concentration of an analyte that can be measured and reported with 99% confidence. 


138 











A MDL was established for each analysis based on at least seven repeated measurements (within 
a run) of a low level standard. 


Table B.4 Precision, accuracy, and recovery for total nitrogen and phosphorous analyses. 

Measure 

Total Nitrogen 

Total Phosphorous 

Precision, % 

98.7 

96.7 

(n=39) 

(n= 36) 

Accuracy, % 

101.8 

97.7 

Digestion Recovery, % 

96.3 

98.5 


Handheld YSI Meters 

The handheld multiparameter Yellow Springs Instruments (YSI) meters were checked 
prior to use with manufacturers (YSI) conductivity standard. To check the dissolved oxygen 
reading the DO probe was placed in a 100% saturated environment for several minutes. If the 
percent saturation value was several points away from 100% the electrodes were cleaned, a new 
KCL solution was reapplied and the membrane was replaced. Another percent saturation reading 
is taken after the DO probe has been serviced. All QA/QC checks and calibrations recorded in a 
database (Microsoft " Office Access 2003). The frequency of checks and calibrations was 
intermittent and depends on how often the units are being used. 

YSI Multiparameter Sonde 

YSI 6600 Multiparameter Sondes were calibrated prior to deployment following the 
manufacturer’s recommendations. Conductivity was calibrated with a one-point calibration 
using standards with conductivity values closest to the expected salinity range (50 mS cm' 1 for 
high salinity stations, 10 mS cm' 1 for mesohaline and 1 mS cm' 1 for low salinity stations). 
Turbidity was calibrated with a two-point calibration; using reverse osmosis water (RO) 
followed by a 123 NTU YSI standard solution. The dissolved oxygen (DO) sensor was 
calibrated for dissolved oxygen in air at sea level using the saturated air in water method. The 
DO anode was cleaned and fresh KCL solution added prior to applying a new membrane film. 
The probes were set in a calibration cup with a small amount of water for maximum water vapor 
saturation for 15 minutes before the calibration reading was taken. The barometric pressure was 
determined from either a mercury barometer or from a YSI 650 or 556 hand-held meter. 

139 


n 









Temperature can not be calibrated but its performance was checked. The data sondes were set in 
a flow through seawater bath in the laboratory for multiple readings immediately before 
deployment and upon return to the lab. The temperature and salinity of the water bath were 
cross-checked using an independent YSI handheld unit (YSI 650 or 556). All QA/QC 
calibration data and ancillary metadata are recorded in an Access database. The calibration 
accuracy for conductivity and turbidity is defined as the accuracy of the probe in a standard 
solution. Post deployment accuracy is defined as the accuracy of the probes after they are 
retrieved from the field and are tested against the known standard solutions. 


Table B.5 Precision and Accuracy (expressed as percentage) of YSI Multiparameter Sondes. 


Temperature 

Salinity 

Conductivity 

Turbidity 

DO 

Calibration Accuracy 

Pre-deployment 

99.0 

98.2 

100.0 

100.0 

99.6 

Post Deployment 

Accuracy (includes the 

effect of biofouling) 

98.7 

96.5 

97.8 

97.1 

94.4 


Burrowing Shrimp Densities 

Shrimp densities were assigned classes (0 holes, 1-10 holes, 11-50 holes, 51-100 holes, 
and 101-175) by direct counts of burrow holes within a 0.25 m“ quadrat. These were designated 
as classes 0-4, respectively. Shrimp burrow identity and density-class, date, time, and 
geographic location data were collected along a survey track using a dynamic line setting 
recording the data onto a March II GPS-data logger (Corvallis Microtechnology Inc., Corvallis, 
OR) at 1-s intervals. As a change in species or density was observed, the line feature was ended, 
and a new dynamic line was started with the new species and density attribute. Burrow identity 
and density classification was verified approximately every 30 min during the survey by 
qualitatively sampling burrows using a bait pump and counting burrow hole densities using a 
0.25 m quadrat. The same procedure was also used whenever the survey team was uncertain of 
burrow attributes. Each ‘QA’ quadrant was also photographed with a digital camera mounted on 
a PVC frame to provide a consistent reference. As a check on burrow hole counting , the burrow 
holes were later re-counted from the digital pictures. 


140 











To quantify the relationships between burrow density class, burrow opening density, and 
shrimp density, spatially coincident “baseline” samples were collected at 90 random sites 
throughout Yaquina estuary. Shrimp density was determined by hydraulically excavating a 
megainfaunal core barrel (40 cm diameter x 100 cm depth) using a suction-dredge, and washing 
the core barrel contents through a 3 mm mesh to retain burrowing shrimp and other large 
infauna. 

Stable Isotope Data 

Macroalgae samples were collected monthly during 2003 and 2004 from five locations 
along the salinity gradient ranging from polyhaline to oligiohaline conditions. Algal material 
was collected from hard substrates to eliminate contamination from any additional nutrient 
sources other than the water column. Five replicates of healthy macroalgae were collected from 
each sampling site. Samplers wore sterile lab gloves while collecting to prevent contamination. 
Each algae sample was washed thoroughly in RO water, frozen and lyophilized. The dried 
material was ground into a fine powder for isotope analysis. Grinding mortar and pestles were 
thoroughly rinsed with acetone and allowed to completely dry between samples (QAPP 02.01, 
2002 ). 

The EPA Integrated Stable Isotope Research Facility (ISIRF) analyzed the samples for 
6 I5 N according to SOP CL-6 (1999). Nitrogen isotope ratios were measured on macroalgae 
samples combusted in a Carla Erba elemental analyzer (model # 1108) equipped with a 4 meter 
poraplot Q gas chromatograph column directly coupled to an isotope ratio mass spectrometer 
operating in a continuous flow mode (Delta S, Finnigan MAT, San Jose, CA, USA). This 
continuous flow mode also provides a direct measurement of nitrogen content. Protocol and 
methods for operation of the mass spectrometer are all based on published approaches that have 
been verified through multiple approach analyses and inter-lab comparisons. A concentration, 
calibration and reference standard were run at the beginning, mid and end of each run. 
Additionally, a spike, concentration standard and blank were run every ten samples. Standard 
material included NBS tomato leaves (1573) for concentration standard; NBS spinach (1570) for 
reference standard; NIST Com Stalk for calibration standard and spike recovery. 


141 


n 


Table B.6 Precision and Accuracy of 5 1 N data. 


5 15 N 

Precision, % 

99.1 (n=23) 

Accuracy, % 

96.6 (n=63) 


Aerial Mapping of Seagrass and Macroalgae 

The remote sensing procedure used in this study to map the intertidal distribution of 
eelgrass and benthic green macroalgae (conducted from 1997 - present) utilizes aerial 
photography with false-color near-infrared (color infrared, OR) film. This allows an aerial 
survey to be conducted during daylight low tide (typical tide level about -0.5 m MLLW) when 
the majority of the eelgrass habitat in the Yaquina Estuary is exposed. CIR film has been found 
to provide substantially better spectral resolution of exposed intertidal vegetation than has true 
color film (Young et al., 1999). The mapping method is able to detect inundated and submerged 
Z. marina to a depth of about 1 m below water level at the time of the aerial photograph. To map 
perennial eelgrass habitat, the surveys are conducted in late spring or early summer before the 
summer bloom of benthic green macroalgae that can interfere with the classification of eelgrass 
habitat. Mid-summer surveys are used to map macroalgal distributions upslope of the eelgrass 
meadows. Photoscales utilized range from about 1:6,000 to 1:20,000. The aerial photographs 
are digitally scanned and georectified while correcting for terrain and camera distortions to 
produced digital orthophotos. The spatial accuracy of the photomap for this estuary (photoscale: 
1:10,000) was assessed by comparing 14 Root Mean Square Error (RMSE) offset values for 
positions of photovisible objects obtained from the photomap, referenced to published National 
Geodetic Survey (NGS) positions. The mean offset was 0.72 m + 0.27 m (95% Cl; Clinton et 
al., in review). The digital orthophotos are classified into eelgrass and bare substrate habitats, 
defined as > 10% cover or < 10% cover, respectively. On-the-ground resolution of 0.25 m is 
obtained in this process. A hybrid technique using both unsupervised and supervised 
classification steps has been developed for this habitat mapping project (Clinton et al., in 
review). The technique requires training data from ground truth surveys, with station positioning 
accomplished by a differential-corrected global positioning system (GPS). The RMSE of GPS 
positions obtained at an NGS first-order monument in Yaquina Estuary was 0.62 m. 


142 







Another part of the ground survey employs a detailed procedure based upon the 
recommendations of Congalton and Green (1999) to provide accuracy assessment data from 
randomly positioned stations within each stratum (Young et ah, in review). Results were 
obtained in spring 2004 from 51 randomly positioned stations within intertidal eelgrass meadows 
and 28 randomly positioned stations within bare substrate strata of Yaquina Estuary. Based upon 
a comparison of results from the image classification with those from the ground survey (taken 
as the reference), application of the classical error matrix analysis yielded an overall accuracy of 
97%, with a Kappa Index value of 0.9447 + 0.0024, indicating excellent agreement (Landis and 
Koch, 1977). The investigators attribute this very high accuracy level to the extensive training 
data provided via GPS mapping of the intertidal eelgrass meadow margins (Young et ah, in 
review). 

Depth Distribution ofZ. marina 

The amount of Z marina at a specific tidal height (Figures 11.2 and 11.3) was 
determined by overlaying the Z. marina maps with the results of an extensive bathymetric depth 
survey conducted by the U.S. Army Corps of Engineers in 2002. The bathymetric model and the 
seagrass classification are both Arclnfo format grids. The bathymetric depth data was 
interpolated onto a grid using TopoGrid in Arclnfo. A 2.0 m floating point bathymetric model 
grid was integerized and resampled to 0.25 cell size to match the binary seagrass classification 
grid. The bathymetric and seagrass grids were then overlaid using the map algebra function 
COMBINE, which produces a grid value attribute table with counts of cells for each unique 
combination of cell values from each grid. 

The aerial mapping method utilized is capable of classifying some submerged seagrass 
beyond the depth at which near-infrared radiation is absorbed by water; however, some of the 
data returned by the COMBINE function is undoubtedly a result of spatial misregistration 
between the aerial photo classification and the bathymetric model. A few outliers were trimmed 
from both tails of the distribution curve. It is also quite possible that a physical survey could 
return some higher percentages of overall seagrass distributions below -1 m MLLW. The result 
of this analysis is presented as distribution curves of total area of intertidal eelgrass for ocean and 
river dominated estuarine areas. 


143 


i f 


Macroalgal Biomass and Cover 

Benthic green macroalgae coverage and biomass measurements were conducted as part 
of several studies in Yaquina Estuary between 1998 and 2004 (Table B.7). Most of the 
measurements were made during the dry season (May - October) in the marine-dominated sector 
(Zone 1). Although both non-random and random sampling designs were employed in different 
survey efforts, all the macroalgal data within Yaquina Estuary (WED unpublished data; Kentula 
and DeWitt, 2004) were combined for analysis. To obtain a regional perspective, the data from 
the Classification Study (Section 4.2) also were summarized for comparison (Lee et al., 2006). 


Table B.7. Sources of ground survey macroalgae data used in this study. All data were 
collected by WED. Random and non-random sampling designs are denoted by R and 
NR, respectively. 

Period 

Zone 

No. of Samples 

Sampling 

Zone 1 

Zone 2 

1998 

1 

69 

- 

NR 

1998-99 

1 

65 

R 

1999-2003 

1 

4159 

- 

R 

2001-2002 

1 

2094 

- 

NR 

2002-2004 

1 

140 

- 

NR 

2004 

1 & 2 

47 

53 

R 


To examine seasonal variability in benthic green macroalgae cover and biomass, six band 
transects were established within Zone 1 of Yaquina Estuary during 1999. These sites (I -VI, 
respectively) were situated 3.9, 5.0, 6.3, 7.8, 8.6, and 10.6 km from the ocean end of the jetties at 
the mouth of the estuary (Figure 2.1). The transects were 30 m wide, and extended 100 m 
upslope from the MLLW tide line perpendicular to the channel. Along each transect, sampling 
stations were regularly spaced at 10 m intervals. At Sites II and III, the bathymetric slope was 
lower than for other sites, and in August 1999 the transects were extended to about 350 m from 
MLLW, with the additional sample stations situated at ~ 40m intervals. Stations along the 
transects were generated with a random number generator prior to field sampling. For each 
station, three randomly-selected distances between 1 and 30 m perpendicular to the sampling 
transect were sampled. Between June and December 1999, most of the sites were sampled every 
other week during daylight low tides, while less frequent sampling of these sites continued 
through May 2000. 


144 















At each plot, a percent cover value within a 0.25 nr quadrant was visually estimated for SAV 
and macroalgae. For much of the data, a frequency-of-occurrence value was also measured by 
recording which class of cover occurs directly beneath each of 25 point intercepts formed by two 
orthogonal sets of string intercepts. The purpose of such frequency-of-occurrence measurements 
was to provide a quality check on the estimated percent cover values. Pilot studies comparing 
the linear regression of the estimated percent cover values against the measured percentage 
frequency of occurrence values have yielded r 2 values of 0.91 - 0.97. For this current study only 
the green macroalgae data was used for analysis. 

Samples of the alga taxa was collected from a 0.05 nr area of the 0.25 nr quadrant at two 
of the three replicates sites along the station line. After cleaning, the alga was identified by 
qualified individuals. Alga keys and identification aides were available for cross-referencing. 
The alga biomass was determined by drying the alga at 80°C until dry. A sample was considered 
dry when there was no further weight change due to loss of water after additional oven drying. 
Returning a subset of sample to the oven after recording the initial dry weight was also a 
measure of repeatability. The final dry weighs for the 0.05 m“ sample was converted into gdw 
nf 2 . 

An alternative method was used to determine macroalgae biomass in the 2002 field 
season. A volumetric biomass estimate was collected according to the graduated cylinder 
method as described in Robbins and Boese 2002. The macroalgae was collected from a 0.25 nr 
quadrant and placed into the 2000 ml cylinder. The algae were pressed with a plunger to remove 
excess water before recording the algal volume in ml. This quick field method was determined 
to be an accurate surrogate for biomass dry weight determination with a linear relationship 
yielding a r 2 value of 0.78 to 0.88 for macroalgae species (Robbins and Boese 2002). 

Zostera Marina Lower Depth Limit 

The lower depth margin of Z marina was determined by georeferencing the position 
where the deepest seagrass was encountered. Transects were randomly selected in distinct Z. 
marina beds (identified from aerial photography) and were approached either by boat or by foot 
depending on the water depth. Sampling was conducted on the lowest tides possible to increase 
the accuracy of locating plants growing at the lowest depth limit. In the deeper systems an 
underwater video camera was mounted on a long PVC pole linked to a video monitor on deck. 

145 


n 


When the first Z. marina patch was seen on the monitor the pole was quickly thrust into the 
sediment to stop the momentum of the boat. A GPS reading was taken and a lead line was used 
to record the depth to the closest centimeter. In shallower waters, seagrass blades were clearly 
apparent on the waters surface and could easily be approached by foot. Personnel walked from 
shore along the transect to the deepest Z. marina patch, recorded a GPS location, measured the 
depth with a lead line and collected other water quality parameters. 

Tidal corrections for Lower Depth Limits of Z. marina 

Tidal corrections were applied to account for variations in tide elevation at time of lower 
limit depth observations, with corrected lower depth limits expressed as depth below mean lower 
low water (MLLW). Tidal predictions were used to make these tidal corrections. Tidal 
predictions are least accurate during storms and extreme low and high tides. Review of weather 
and tidal conditions during time periods when the lower depth limit of Z. marina was measured, 
suggests that conditions were relatively calm during the sampling and not collected during 
extreme tides. In addition, the difference between predicted and observed tidal heights at the 
South Beach tide gauge was less than 0.15 m on all data collection days. Tidal predictions that 
take into account variations in amplitude and phase lags of tides are available for four locations 
(South Beach, Yaquina, Winant, and Toledo) in the Yaquina Estuary (http://co- 
ops.nos.noaa.gov/tides05/tab2wclb.html# 132). Tidal heights relative to MLLW were calculated 
using WXTIDE 32 for each depth site using the time of data collection and the nearest tide 
prediction location. The maximum distance along the river any given depth station from a tide 
station was 4.5 km. The largest source of error in the tidal corrections results from not having 
predicted tides available for all locations along the longitudinal axis of the estuary and having to 
use the closest tide prediction station. To estimate the error associated with the tidal correction, 
we compared the differences in tidal corrections between the two stations that are located 
upstream and down stream of the observations. The error associated with the tidal correction is 
estimated to be 0.1-0.2 m. The error associated with the tidal correction increases with distance 
from the mouth of the estuary, being a minimum of 0.1 m in the lower estuary and as high as 0.2 
m near Toledo. 


146 


Epiphyte Methods 

Epiphytes growing on Z marina leaves were collected within the Yaquina Estuary from 
2000 though 2004. Data were collected at six stations distributed between 3.5 and 17 km upriver 
from the mouth of the Yaquina Estuary. Leaves from collected plants were subdivided into outer 
(older) and inner (younger) leaves. Epiphytes were scraped from these leaf groups with dry 
weights (24-36 hours at 60-70 °C) of the removed material determined for each individual plant. 
The effect of epiphyte cover in terms of reducing light (PAR) availability to eelgrass was 
estimated in the laboratory using a LI-COR LI-190S A quantum sensor. Freshly removed 
epiphytes from a single leaf were washed into a Plexiglas cylinder with distilled water (60 mL). 

A light source was placed above this cylinder with the PAR sensor below the chamber and the 
amount of irradiance was determined. This value was then compared to a similarly measured 
irradiance value obtained using the same cylinder containing 60 mL of distilled water without 
epiphytes. 

Station Location 

The geoposition of each station was collected in the field with a Global Positioning 
System (GPS) and differentially corrected in post processing with data form the nearest National 
Geodetic Survey (NGS) Continuously Operating Reference Station (CORS). The GPS data 
collection device (CMT March II) published post-processed differentially corrected two 
dimension root mean square error (spatial accuracy) ranges from 1-5 m. 

Distance Upriver Calculations 

The distance from the mouth of the Yaquina to each station was calculated using GIS 
mapping software Arcview. After a center line shape file extending the length of the river was 
converted into a route file, the orthogonal distance from each station to the nearest point along 
that route file was calculated. The ArcToolBox linear referencing tool, 

LocateFeaturesAlongRoutes, was used to calculate the distances in meters for each station from 
the mouth of the estuary to the nearest point along the centerline route feature. 


147 


11- 


Landscape Data 

The Yaquina landscape analysis was done as part of a larger Pacific west coast estuary 
survey. Watershed boundaries subtending the Yaquina estuary basin were primarily determined 
from the Sixth Field hydrologic unit code (HUC) geospatial layer created by the Forest Service 
from 1:24,000 scale USGS maps, digital elevation models and other data sources. 
( http://www.reo.gov/gis/proiects/watersheds/REOFIUCvl 3.htm ) was used as a primary 
reference. In Oregon, the Forest Service and Oregon State University have produced a 
watershed layer refined to the 7th field HUC boundary lines for most of coastal Oregon 
(h ttp:// www. fsl. orst. edu/c lam s/cfs1023 3. h tm 1 ) north of the Rogue River-Refinements to the 
drainage boundaries between coastal and estuarine basins were often based on review of the 
hydrologic drainage patterns derived from digital elevation data (10 meter resolution in Oregon) 
and from USGS 1:24,000 scale quadrangle maps. Boundary lines and water bodies were plotted 
and reviewed for accuracy of coding and fidelity to the original sources. The Yaquina watershed 
delineated in this project captures the entire drainage area (EDA). By delineating the entire 
watershed, the watershed area is equivalent to the sum of NOAA’s Estuarine Drainage Area 
(EDA, portion of watershed that empties directly into the estuary and is affected by tides) and 
Fluvial Drainage Area (FDA, portion of an estuary's watershed upstream of the EDA boundary; 
see http://spo.nos.noaa.goV/proiects/cads/description.html#cafj . 

Land Cover Sources 

The estuary watershed was used as clipping boundaries for several land use/land cover 
datasets that are available for the Pacific coastal region at this time. The National Land Cover 
Data (NLCD, http://www.mrlc. govj represents land cover circa 1992 and its extent is 
nationwide. This dataset was clipped to the Yaquina watershed boundary. The 1992 NLCD data 
contains 21 classes of land cover (see http://erg.usgs.gov/isb/pubs/factsheets/fslQ800.pdfj . The 
area of each land cover class in square kilometers and as a percentage of the watershed was 
calculated and entered into an Access database. 

Two additional land use datasets have been created by the NOAA’s Coastal Services Center (C- 
CAP, http://www.csc.noaa.gov/crs/1 ca/ccap.htm 1 j program. The more recent data were derived 
from late 2000 and 2001 Landsat TM (thematic mapper) imagery. NOAA also produced a layer 
from imagery collected circa 1995-1996 and the earlier dataset was used to generate a land cover 


148 








change layer. The 2001 NOAA data are based on 22 land use classes 

(http://www.csc.noaa.gov/crs/lca/oldscheme.html), which are not exactly the same as those used 
in the NLCD. In January 2007, the Multi-Resolution Land Characteristics Consortium (MRLC, 
http://www.mrlc.gov ) released a new national land cover data, NLCD 2001. The areas classified 
by the NOAA C-Cap program were incorporated into the 2001 release. Procedures used in the 
development of the 2001 land cover data layer are presented in Homer et al (2004). The land 
cover in NLCD 2001 is based on 30-meter resolution data derived from Landsat imagery and 
uses 21 classes that are a modified version of the land classes used in the 1992 NCLD analysis 
(see http://www.eDa.gov/mrlc/classification.htnii for a crosswalk of the two schemes). 

Land Use Patterns and Watershed Characteristics 

Land cover data from the 1992 and 2001 MRLC NLCD data and from the NOAA 1995 and 2001 
data were used to calculate the area and percentage of the watershed for each of the 21 (NLCD) 
or 22 (NOAA) land use classes. Accuracy of the 1992 NLCD data by EPA region is presented at 
http://landcover.usgs.gov/accuracv/index.php . Based on this analysis, users were cautioned about 
applying the data to highly localized studies, such as over a small a watershed. Accuracy of the 
NLCD data of the MRLC zone that contains the Yaquina watershed is estimated to be 86.1%. 
These data sets were used to generate estimates of the area of impervious surfaces in each 
watershed using default coefficients from the Analytical Tools Interface for Landscape 
Assessments (ATtILA) software (U.S. EPA, 2004b). The MRLC 2001 impervious surface layer, 
which represents an estimate of developed impervious surface per pixel by percent 
imperviousness, was clipped and summarized for the watershed. Overall accuracies for the 
impervious surfaces from the 2001 MRLC data range from 83 to 91 percent (Homer et al., 2004; 
Yang et al. 2003), and represent a higher resolution estimate of impervious surfaces than 
available from ATtiLA. Estimates of nitrogen and phosphorus loadings from land use were 
calculated from the watershed land cover data using coefficients from the ATtiLA program. 

Estimates of slope were calculated for each watershed from slope surfaces generated 
from 10 meter (Oregon) DEMS (digital elevation models). Mean slope by percent and by 
degrees for land surfaces above the mean high water level were calculated and all slope values 
were exported to an Access database. The 30 meter DEMS were obtained from the National 
Elevation Dataset (NED, http://ned.usgs.gov) , a seamless mosaic of the best elevation data. The 

149 


n 








10 meter elevation data for Oregon was obtained by the USDA Forest Service for the Coastal 
Landscape Analysis and Modeling Study project (CLAMS, http://www.fsl.orst.edu/clams ) from 
USGS drainage enforced digital elevation models. 

Population Density 

Human population estimates from the 1990 and 2000 censuses (http://www.census.govA 
were generated for the drainage unit. Area weighted estimates of total population by census 
block were summed for each drainage and population density (individuals/sq. kilometer) was 
calculated from the total drainage population estimate. 

Other Data Sources 

Historical sources (data collected in the 1960, 70 and 80’s and presented in technical 
reports, thesis dissertations or journal publications) were carefully reviewed for information on 
quality procedures implemented in their data collection. Methodological approaches for 
measuring DO, chlorophyll-#, and nutrients were reviewed for analytical method utilized. Data 
obtained from the Oregon Department of Environmental Quality database were previously QAed 
and only data with grades of A and A+ were used as part of this report. Often historical data 
collected before the use of GPS systems were given as locations on map, common station names, 
or as kilometers upriver. From these descriptions, the northing and easting UTM locations were 
estimated using Topozone or Google Earth. Locations given in Latitude and Longitude were 
converted into UTM units using a batch converter located at 

http://www.uwgb.edu/dutchs/UsefulData/HowUseExcel.HTM or in Topozone. Units were 
converted to metric units (i.e. feet to meters, Fahrenheit to Celsius, etc.). Most of the data was 
hand entered from tables to electronic format in Excel. Data was entered electronically by one 
person and checked for errors by another independent person. Data points presented only in 
graphical format were digitized into electronic format and translated into tables. Errors and any 
resulting changes were documented and traced to the source. All data were entered into an 
Access database and data were reviewed to ensure that there were no duplicate entries. 


150 




Models 

Stress-Response Model 

All field and laboratory data used in the development of the Stress-Response model 
(SRM) were collected in accordance with WED SOP’s and QAPP’s. (Tables B.l and B.2). A 
complete list of these parameters is found in Kaldy and Eldridge (2006) Volume II-Tables 1, 2, 

3, 4, and 5. All the measurements of physical and biological quantities used in our modeling are 
subject to uncertainties. Further these measurements were often combined to produce new 
derived quantities, each of which has its own uncertainties. We calculated these uncertainties 
using means and standard deviations of the data and error propagation algorithms from 
http://teacher.nsrl.rochester.edu/Dhy labs/ADDendixB/AppendixB.html . 

Once the SRM was calibrated to the biomass data (see Kaldy and Eldridge 2006, 

Figure F.l), we conducted a series of tests to examine the models sensitivity to parameters. A 
model that is overly sensitive to parameters is considered unstable. Kaldy and Eldridge (2006) 
Volume II-Table 8 provide a complete sensitivity analysis of the model used herein and a 
discussion of the sensitive results is presented on page 55 of Kaldy and Eldridge (2006). 

Another aspect of the model quality assurance is the development of validation 
experiments. We developed the model using local seagrass and environmental data, but plan to 
use the model to address regional or national level questions. Z. marina physiology and genetic 
alleles (minor variations of the same gene) diverge significantly in different regions of the 
continental United States. The regional differences in Z. marina require that we run our 
validation experiments at multiple scales. At the local scale we have developed plant level-tracer 
experiments to evaluate the allocation of carbon within a plant (Kaldy and Eldridge 2006). At 
the regional scale we are planning running validation experiment in Puget Sound, Washington 
during 2007. At the national scale we have conducted Z. marina mesocosm experiment in 
Narragansett, Rhode Island in collaboration with AED and the University of Rhode Island. 

These data will be used to validate or recalibrate the SRM for regional or national level 
implementation of the SRM. The combination of the data uncertainty analysis, the model 
calibration and sensitivity analyses, and the local, regional, and national scale SRM validations 
constitute our QC/QA program. 


n 


151 



Hydrodynamic and Nutrient Source Model 

All field and laboratory data used in the development of hydrodynamic and nutrient 
source model were collected in accordance with WED SOP's and QAPP’s. (Tables B.l and B.2). 
A two-dimensional, laterally-averaged hydrodynamic and water quality model (Cole and Wells 
2000) was used to simulate the transport of riverine, oceanic and wastewater treatment facility 
(WWTF) effluent dissolved inorganic nitrogen (DIN) sources. This model is well suited for 
long-narrow estuaries, such as Yaquina Bay, where there are minimal lateral variations in water 
column properties. U.S. EPA (2001) suggested that this model may be useful in the estuarine 
nutrient criteria development and has been used in developing estuarine Total Maximum Daily 


Loads (TMDLS). 

In the model simulations presented in this study, Yaquina Estuary was represented by 325 
longitudinal segments spaced approximately 100-m apart with each longitudinal segment having 
1-m vertical layers. The model domain extended about 37 km from the tidal fresh portion of the 
estuary at Elk City, Oregon to the mouth of the estuary. Model simulations were performed for 
the interval January 1 to October 1 of 2003 and 2004 and included tidal and wind forcing as well 
as freshwater inflow. Parameters simulated included water surface elevation, salinity, water 
temperature, and DIN. 

Model calibration is the process of determining model parameters that are appropriate for 
the specific study location and time interval being simulated. The model used in this study was 
calibrated through adjustment of friction coefficient, eddy viscosity, and eddy diffusivity. To 
assess the model performance at simulating the hydrodynamics, we compared simulated and 
observed water level variations at two locations in the estuary and salinity and water temperature 
at four locations utilizing data from the YSI datasondes. Since the datasondes used at these 
stations were not leveled in we could only compare relative water level fluctuations, not absolute 
water level (referenced to MLLW). In addition, temperature and salinity from the CTD cruises 
were compared to simulated values. The model was assessed by calculating the root mean 
square error between observed and predicted variables. 

Each nitrogen source, riverine (N river ), oceanic (N ocean ), and WWTF effluent was 

modeled as a separate component. The nitrogen sources were modeled as 


dN 

dt 


transport — pN 


152 



where N is the DIN source and |j. is a loss/uptake rate. The same value of ja was used for all three 
nitrogen sources and the value of p was determined by fitting total modeled DIN 
(Nocean+Nriver+Nwwtj) to observations of DIN within the estuary. The best fit to observations was 
found with p = 0.1 d' 1 . Simulations were also performed with no uptake (p = 0) which is 
equivalent to conservative transport of the sources. The results from the transport model were 
used to mix the three nitrogen sources using the following equation 

$m ~ //A + fo$o + fw$w 

Ir +fw + fo=\ 

where fR,fw, and fo are the fractions of riverine, wastewater treatment facility, and oceanic DIN, 
respectively, and 5r, 5w, and 5r are the isotopic end members for riverine, wastewater treatment 
facility effluent, and oceanic sources, respectively. Estimates of the oceanic and riverine end 
members were obtained by examination of the observed isotope ratios at the stations located near 
the mouth of the estuary and in the riverine portion of the estuary and comparison to the 
literature. The initial estimate for the WWTF end member (8w = 15-22%o) was determined from 
the literature. To arrive at the final end member isotope ratios, model simulations were 
performed varying each end member over the range estimated from the data and literature. The 
final isotope ratio of end members for the three sources (5r=2%o, 5w = 20%o, and 5o=8.4%o) was 
determined from the best fit (minimum root mean square error, RMSE) between predicted and 
observed isotope ratio at the five isotope sampling stations during 2003 and 2004. The final 
oceanic end member selected is consistent with marine end members for the west coast of the 
United States (Fry et al. 2001). While the riverine end member is consistent with the isotope 
ratio expected for nitrogen associated with red alder (leaf tissue ranges between -3 and -0.5% o ; 
Hobbie et al. 2000; Tjepkema et al. 2000; Cloem et al. 2002). 

List of Quality Assurance Project Plans (QAPPs) Used in This Study 

QAPP98.04. Evaluation of the Susceptibility of Eelgrass Beds in Oregon Estuaries to Changes in 
Watershed Uses. R. J. Ozretich, EPA, 1998. 

QAPP 2000.01. Changes in the Abundance and Distribution of Estuarine Keystone Species in 
Response to Multiple Abiotic Stressors. T.H. DeWitt, EPA, 2000. 

QAPP 01.02. Modeling of Landscape Change Effects on Estuarine Trophodynamics: an 

Optimization Approach Using Inverse and Forward Modeling. P. Eldridge, EPA, 2001. 

153 




QAPP 01.04. Assessment of the Spatial and Temporal Distribution of Submersed Aquatic 

Vegetation and Benthic Amphipods within the Intertidal Zone of Yaquina Bay Estuary, 
Oregon via Color Infrared Aerial Photography. D.R. Young, EPA, 2001. 

QAPP 01.06. Upper Margin Expansion: Influences on Seagrass, Zostera marina L. B.L. Boese, 
EPA, 2001. 

QAPP 02.01. Autecological studies of marine macrophytes including the sea grasses Zostera 
marina and Z. japonica in Yaquina Bay, Oregon. J. Kaldy, EPA, 2002. 

QAPP 04.01. Seagrass Research - Epigrowth Light Attenuation Task: Estimation of spatial and 
temporal variation in light attenuation due to epigrowth on Zostera marina in Yaquina 
Bay. W. Nelson, EPA, 2004. 

Marine Science Institute Analytical Laboratory University of California, Santa Barbara. Quality 
Assurance Manual-Draft. 2005. 


List of Standard Operating Procedures (SOPs) Used in This Study 

CL -6. V.2. Standard Operating Procedures for Stable Isotope Ratio Mass Spectrometer Analysis 
of Organic Material. EPA. 1999. 

MES EPOl.rev 0. Draft. Standard Operating Procedure for Collecting and Processing Zostera 
marina and Associated Epiphytes for Light Attenuation Measurements. Dynamac 
Corporation. 2004. 

MES SOP09.rev 0. Standard Operating Procedure for Preparing Water Samples for Nutrient 
Analysis. Dynamac Corporation. 2003. 

MES SOP02.rev 0. Standard Operating Procedures for Weighing Food Web Samples and 
Submitting them to ISIRF for Stable Isotope Analysis. K. Rodecap, Dynamac 
Corporation. 2002. 

WED SOP06.rev 0. Standard Operating Procedure for Preparation and Analysis of Estuarine 
Water Samples for Determination of Chlorophyll-a Content. Dynamac Corporation. 

2004. 

SOPIOP.09. Operating Procedure For YSI Series 6 Multiparameter Water Quality Meters, Model 
#s 6000UPG and 6600, 6600EDS. D.T. Specht. EPA. 2004. 

SOPFSP.01. Use of The Seabird Seacat (SBE-19) Ctd Package. R. J. Ozretich. EPA. 1999. 


154 


WRS 14B.2. Standard Operating Procedure for the Determination of Total Suspended Solids 
(Non- Filterable Residue). Dynamac Corporation 2005. 

WRS 34A.3.Standard Operating Procedure for the Digestion and Analysis of Fresh Water 
Samples for Total Nitrogen and Total Phosphorus. 2005. 

Clinton, P.J., Young, D.R., and Specht, D.T. In Review. Standard Operating Procedures for 

producing digital aerial photomaps of estuarine intertidal ecosystems using color infrared 
film, classifying eelgrass and non-vegetated habitats, and assessing the accuracy of the 
classifications. SOP-NHEERL/WED/PCEB/PJC/06-01-000 09/15/06, U.S. 
Environmental Protection Agency, Pacific Coastal Ecology Branch, Newport, OR. 


155 


a- 


Appendix C: Classification of Oregon Estuaries 

Of the estuaries that were sampled as part of the Classification and NCA data sets, the 
number of classes of estuaries (or types) depends upon the scale of the classification system as 
well as the classification system utilized (see Table C.l). Of the seven estuaries sampled by 
WED in 2004-2005 which form the Classification data set, five were also included in a NOAA 
classification scheme, with 4 estuaries classified as “river dominated with straits and terminal 
bay”, and 1 estuary classified as “coastal embayment - v-shaped and semienclosed.” Lee et al. 
(2006) classified all seven of the estuaries sampled in the Classification effort as “drowned river 
valley” and Bottom et al. (1979) classified 6 of them as “partially mixed” and the 7 th (Coos) as 
“well mixed.” For the Oregon estuaries sampled as part of the NCA effort, the NOAA 
classification would define eight as “river dominated with straits and terminal bay”; one as “river 
dominated with salt wedge” (Columbia), one as “coastal embayment” (Coos), and one as 
“lagoon” (Netarts). 

Quinn et al. (1991) classified West Coast estuaries based on their relative susceptibility to 
nutrient pollution, defined as an estuary’s capability to concentrate dissolved and particulate 
pollutants. In their 1991 study, Quinn et al. classified the estuaries by dissolved concentration 
potential (DCP), which is the ability of the estuary to concentrate dissolved substances, and 
particle retention efficiency (PRE), which is a measure of the ability to retain suspended 
particulates within the estuary. In this classification system, 8 Oregon estuaries (including Alsea, 
Coos, Nehalem, Netarts, and Siletz, Siuslaw, Tillamook, and Yaquina) classified as having 
“high” DCP and “low” PRE. Umpqua Estuary classified as “medium” (near border of high) 

DCP and “low” PRE, while the Columbia River Estuary had “low” DCP and “low” PRE. All of 
the Oregon estuaries classified as being in the medium category of nitrogen concentrations 
(estimated using the loadings and DCP). 

Burgess et al. (2004), Engle et al. (2007), and Bricker et al. (in prep.) classified estuaries 
of the United States using statistical cluster analysis of physical and hydrologic variables to 
determine the response of estuaries to nutrient loading. The Burgess et al. (2004) classification 
included physical and hydrologic parameters including estuarine area, estuary drainage area, area 
of mixing, seawater and tidal fresh portions of the estuary, tide, overflow, estuary volume, tidal 
prism volume, salinity, depth, DCP and PRE. The primary variables contributing to the 
separation of 11 clusters (or classes of estuaries) in the Burgess et al. (2004) classification were 


156 


size of the estuarine drainage area, estuary area and volume, riverflow, depth and salinity. In a 
more recent estuarine classification, Engle et al. (2007) updated the Burgess et al. (2004) 
classification scheme to incorporate average air and water temperature and surface and bottom 
water temperature and found that the estuaries clustered into 9 classes. Bricker et al. (in prep.) 
also used a cluster analysis to classify the same estuaries and they found that the estuaries were 
best clustered by estuary depth, tide, ratio of freshwater input to estuary area, temperature, and 
mouth openness, resulting in a classification with 10 classes of estuaries. 

Using the Engle et al. (2007) classification, the estuaries sampled in the Classification 
data set fall into two classes (Alsea and Umpqua in one; and Coos, Tillamook, and Yaquina in a 
second class). In the Bricker et al. classification, the estuaries sampled in the Classification data 
set fall into two classes; however, Alsea, Tillamook, Umpqua and Yaquina are in one class and 
Coos is in another. The Oregon estuaries sampled in the NCA data set fall into 3 classes in the 
Engle et al. (2007) classification with 8 of the 11 classified as within one class, while in the 
Bricker et al. classification these same estuaries fell into 2 classes with 9 of the 11 being in the 
same class. 


157 


n 


fication Study and NCA data sets (data set denoted as C and N, respectively in first 
httn://www.coastalatlas.net) and Bottom et al. (1979). 

Quinn et.al (1991) 

Predicted 

Concentrations 

o. 

2 

2 

2 

X 

NA 

X 

2 

NA 

2 

X 


2 

VN 

2 

Legend: NA denotes classification not available; H, M, and L represent high, medium, and low classes (respectively). 

Z 



2 

s 

NA 

2 


NA 





NA 

2 

Particle 

Retention 

Efficiency 

_i 

X 

X 

x 

NA 

X 

X 

NA 

X 

X 

X 

X 

NA 

X 

Dissolved 

Concentration 

Potential 

x 

x 

X 

X 

NA 

X 


NA 

— 

X 

X 


NA 

X 

Bricker et 
al. (in 
prep.) 



— 


NA 

— 


NA 





NA 


Engle et 
al. 

(2007) 

— 


oo 

oo 

NA 

oo 

oo 

NA 

OO 

oo 

00 

— 

NA 

oo 

Burgess 
et al. 
(2004) 

ON 

— 

NO 

On 

NA 

— 

ON 

NA 

On 

On 

ON 

ON 

NA 

NO 

Table C.l. Classifications of 14 Oregon estuaries sampled in WED Classi 
column). Lee et al. (2006) Classification based on Oregon Coastal Atlas (! 

Stratification 
Bottom et al. 
(1979) 

Partially 

Mixed 

NA 

Well Mixed 

Partially 

Mixed 

Partially 

Mixed 

Well Mixed 

Partially 

Mixed 

Partially 

Mixed 

Partially 

Mixed 

Partially 

Mixed 

Partially 

Mixed 

Partially 

Mixed 

NA 

Partially 

Mixed 

Geomorphology 
Lee et al (2006) 

Tidal dominated 
drowned river 

River dominated 
drowned river 

Tidal dominated 
drowned river 

Tidal dominated 
drowned river 

Tidal dominated 
drowned river 

Bar built 

River dominated 
drowned river 

Bar built or 
drowned river 

Tidal dominated 
drowned river 

Tidal dominated 
drowned river 

Tidal dominated 
drowned river 

River dominated 
drowned river 

Tidally Restricted 
coastal Creek 

Tidal dominated 
drowned river 

WON 

Type 

Straits w/ 
Term. Bay 

Salt Wedge 

V-shaped & 
semi-encl. 

Straits w/ 
Term. Bay 

NA 

Limited 

FW Inflow 

Straits w/ 
Term. Bay 

NA 

Straits w/ 
Term. Bay 

Straits w/ 
Term. Bay 

Straits w/ 
Term. Bay 

Straits w/ 
Term. Bay 

VN 

Straits w/ 
Term. Bay 

Class 

River 

Dominated 

River 

Dominated 

Coastal 

Embayment 

River 

Dominated 

NA 

Lagoons 

River 

Dominated 

NA 

River 

Dominated 

River 

Dominated 

River 

Dominated 

River 

Dominated 

NA 

River 

Dominated 

Estuary 

Alsea 

(C,N) 

Columbia 

(N) 

Coos 

(C,N) 

Nehalem 

(N) 

Nestucca 

(C,N) 

Netarts 

(N) 

Rogue 

(N) 

Salmon R. 
(C,N) 

Siletz 

(N) 

Siuslaw 

(N) 

Tillamook 

(C,N) 

Umpqua 

(C,N) 

Yachats 

(N) 

Yaquina 

(C,N) 



































Appendix D: Survey of Effects of Macroalgae on Biota 


Publications concerning the ecological effects of macroalgae were reviewed, and reported 
threshold values of percent cover and/or biomass for effects on infauna, epifauna, fishes, and 
shorebirds are summarized in Table D.2 


159 


> ^ 


Table D.2. Summary of literature regarding the effects of macroalgae (biomass or percent cover) on 
estuarine infauna, epifauna, fishes, and shorebirds. 

Taxa 

Type 

Location 

T 

(° C) 

Sediment 
H 2 S or Low 
Eh 

Effect* 

Macroalgal 

Density 

Citation 

Shorebirds 

S 

England 
(S. coast) 


Y 

■i abundance 

Cover: 75% in 
20% of 
intertidal 

Tubbs 

(1977) 

Shorebirds 

S 

England 
(S. coast) 


Y 

Neg. correl.: areas 
densest algal mats vs. 
abundance 

Cover: >25% in 
40% of 
intertidal 

Tubbs & 

Tubbs 

(1980) 

Infauna 

Epi. fauna 
Shorebirds 

s 

England 
(S. coast) 


Y 

i infauna 

T epibenth.fauna 
i shorebirds 

Cover: 42 % 

Nicholls et al. 
(1981) 

Infauna 

Shorebirds 

s 

England 
(S. coast) 



Little effect on 
zoobenthos or shorebirds 

~ 300 gdw m' 2 

Soulsby et al. 
(1982) 

Shorebirds 

s 

England 
(S. coast) 



i abundance 
(refutes Soulsby et al., 
1982) 

Cover: >25% in 
40% of 
intertidal 

Tubbs & 

Tubbs 

(1983) 

Infauna/ 

Epi.fauna 

F 

Ireland 
(S.W. coast) 


Y 

i infauna 

T epi.fauna 


Thrush 

(1986) 

Infauna 

F 

Scotland 
(E. coast) 


Y 

xl amphipods 
t polych. & bivalves 

60 gdw m*“ 

Hull 

(1987) 

Infauna 

F 

Sweden 
(S. coast) 

~20 


i larval settlement 

143 gdw m' 2 

Olafsson 

(1988) 

Infauna 

S 

Scotland 
(E. coast) 



Altered infaunal 
composition 

440 gdw m' 2 

Raffaelli et al. 
(1989) 

Infauna 

F 

Central 

California 


Y 

i bivalves 
xl phoronids 

800 gdw m' 2 
Cover: 100 % 

Everett 

(1991) 

Infauna 

F 

Scotland 
(E. coast) 


Y 

xl amphipods 
by ~90 % 

200 gdw m' 2 

Raffaelli et al. 
(1991) 

Infauna 

S 

North Baltic 
Sea 

~16 


xl bivalves by 73 % 

832 gdw m' 2 

Bonsdorff 

(1992) 

Epibenthic 

fauna 

S 

Sweden 
(W. coast) 

14-20 


xl epi.fauna by > 50 % 

Epiphyte cover 
on eelgrass: 

80% 

Isaksson & 

Pihl (1992) 

Juvenile 

flatfish 

s 

Sweden (W. 
coast) 

4-20 


xl abundance 

Cover: 50% 

Pihl & van der 
Veer (1992) 

Infauna 

F 

Central 

California 



1 bivalves by ~ 70 % 

220 gdw m' 2 

Cover: 100 % 

Everett 

(1994) 

Decapods 

Pred. Fish 

F 

Sweden 
(W. coast) 

10-20 


T decapods 
xl foraging 

Cover: 35 % 
Cover: 75 % 

Isaksson et al. 
(1994) 

Fishes 

S 

Sweden 
(W. coast) 

14-20 


i fish biomass 

7-225 
gdw m' 2 

Pihl et al. 

(1994) 

Juvenile 

Flatfish 

L 

S 

Sweden 
(W. coast) 

9-12 


xl settling 
xl abundance 

Cover: 80 % 

179 gdw m' 2 

Wennhage & 

Pihl 

(1994) 

Epibenthic 

fauna 

Juvenile 

Flatfish 

S 

Sweden 
(W. coast) 

14-20 


T epi.fauna 
xl epi.fauna 
xl fish sp. & foraging 

Cover:30-50% 
Cover: 90% 
Cover:30-40% 

Pihl et al. 

(1995) 

Shorebirds 

S 

Portugal 



No effect on feeding 

30-60 gdw m' 2 

Murias 
et al. (1996) 


160 































Zoobenthos 

F 

North Baltic 
Sea 

11-16 

Y 

vl abundance 
& biomass by 87-94 % 

440 gdw m' 2 

Norkko & 
Bonsdorff 
(1996a) 

Gastropod 

& bivalve 
Mollusks 

F 

North Baltic 
Sea 

11-16 

Y 

i abundance & biomass 

440 gdw m' 2 

Norkko & 
Bonsdorff 
(1996b) 

Infaunal 

bivalve 

&shrimp 

L 

North Baltic 
Sea 

6-20 

Y 

i survival by 80% 

1 survival by 83% 

200 gdw nf 2 

440 gdw m' 2 

Norkko & 
Bonsdorff 
(1996c) 

Zoobenthos 

S 

North Baltic 
Sea 


Y 

Alters community 

300 gdw m' 2 

Bonsdorff 
et al. (1997) 

Zoobenthos 

F 

Scotland 
(E. coast) 


Y 

■l abundance of invert, 
prey of fishes/birds 

<600 
gdw m‘ 

Raffaelli et al. 
(1998) 

Infauna 

L 

North Baltic 
Sea 

20 


i juv. bivalve moll. 

Cover: 50 % 

Norkko 

(1998) 

Infauna 

S 

Maine, 

U.S.A. 


Y 

Shifted community 
structure to detritivores 

50-200 
gdw m' 2 

Thiel & 

Watling 

(1998) 

Infauna 

F 

Scotland 
(E. coast) 


Y 

Altered infauna 

200 gdw nf 2 

Bolam et al. 
(2000) 

Infauna 

F 

Portugal 



T infaunal 
detritivores 

158 gdw m" 

Lopes et al. 
(2000) 

Zoobenthos 

S 

L. 

North Baltic 
Sea 

19 

Y 

Altered structure. 
i bivalve & amphipod 

181 gdw m" 

Norkko et al. 
(2000) 

Amphipods 

S 

Portugal 


Y 

Population 

zeroed by algae crash 

413 gd w m' 2 ** 

Pardal 
et al. (2000) 

Shorebird 

Black-tailed 

Godwit 

S 

Ireland 
(S. coast) 



i abundance 

Cover: 

40-70% 

Lewis & 

Kelly (2001) 

Infauna 

F 

Sweden 
(W. coast) 

17-18 

Y 

1 suspension feeding 
bivalves by -90 % 

300 gdw m' 2 

Osterling & 

Pihl (2001) 

Epibenthic 

Mudsnail 

S 

Portugal 
(W. coast) 



T abundance 

250 gdw m' 2 ** 

Cardoso 
et al. (2002) 

Copepods 

F 

New York, 
U.S.A. 


Y 

i copepods by -85% 

100 gdw m' 2 

Franz & 

Friedman 

(2002) 

Infauna 

Pred. birds 

F 

Ireland 
(S. coast) 



vl amphipod abundance; 
Black-headed gulls 
avoid algal cover 

206-277 
gdw m‘ 

Lewis 

et al. (2003) 

Zoobenthos 

F 

Portugal 
(W. coast) 


Y 

T abundance mudsnail, 
polych. worm 

600 gdw m' 2 

Cardoso 
et al. (2004) 

Infauna 

F 

Australia 
(E. coast) 


Y 

i polychaete, molluscan 

abundance 

450 gdw m' 2 

Cummins et 
al. (2004) 

Infauna 

S 

England (S. 
coast) 


Y 

vt infaunal abundance 

-70% 

i infaunal biomass 70% 

Cover: 90 % 

Jones & Pinn 
(2006) 

Zoobenthos 

S 

NE Baltic 

Sea 



Minor alterations to 
community structure 

347 gdw m' 2 

Cover -25% 

Lauringson& 
Kotta (2006) 

Infauna 

F 

Netherlands 



No major changes in 
infauna 

80 gdw m' 2 

buried in 
sediment 

Rossi (2006) 

Type of Study: S is survey, F is field manipulation, L is lab experiment; Effects: i decreased; T increased; 

* For survevs. negative correlation suggesting a possible effect; ** Ash free dry weight 


' t 


161 


































Carbohydrate (gNSC m" 2 ) Biomass (gdw m' 2 ) 


Appendix E: Stressor-Response Model Calibration, Input Data, and Results 




Month 

Figure E. 1 Stressor-Response Model Calibration for biomass and carbohydrate during 2002 in 
Zoster a marina. Error bars represent standard errors. 


162 




































Photon Flux Density 
(nmol m" 2 s’ 1 ) 


Zone 1 
Zone 2 




Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

Month 


Figure E.2 Environmental input data (temperature, salinity and photon flux density) used in the 
SRM simulations for Zone 1 (lower estuary) and Zone 2 (upper estuary). Data presented 
are a composite created by averaging data from 1999-2003. 


' i- 


163 














0 m 
-1 m 



^ ^ # d 5 " 'f v 9 '' ^ cP 'f # cP 'f ^ # o' 5 " 

Year 1 Year 2 Year 3 Year 4 


Figure E.3 Case 1, Zone 1; simulations using median light attenuation and DIN values. Zostera 
marina biomass and carbohydrate trajectories for depths from 0 to 2 m below MLLW are 
stable indicating that median values are protective in Zone 1. At depths greater than 2 m 
below MLLW, the trajectories indicate that the median values are not protective. 


164 
















0 m 
-0.5 m 



^ ^ ^ ^ ^ ^ ^ ^ 

Year 1 Year 2 Year 3 Year 4 


Figure E.4 Case 1, Zone 2; simulations using median light attenuation and DIN values. Stable 
Zoster a marina biomass and carbohydrate trajectories indicate that the median values are 
protective in Zone 2 for depths between 0 and 0.5 m below MLLW. Simulations at the 1 
m depth contour appear to be more closely associated with the deeper depths (1.5 to 2.5 
m below MLLW) suggesting that this is a break-point that would be susceptible to 
decline from minor perturbation. At depths below 1 m MLLW, the trajectories indicate 
that the median values are not protective. 


11 - 


165 
















C/D 

0 

CD 

s_ 

~a 
>> 
_c 
o 

_Q 

CD 

o 


E 

O 

O 

(f) 


300 


200 


S 100 


^ ^ ^ ^ ^ 

Year 1 Year 2 


Year 3 


Year 4 


200 


150 

100 

50 

0 

400 


-0 m 

-1 m 

-_ 2 m 

- • • • -3 m 
--4 m 


fh • 

Figure E.5 Case 2, Zone 1; simulations using 25 percentile light attenuation and DIN values. 

iL 

Stable Zoster a marina biomass and carbohydrate trajectories indicate that the 25 
percentile values are protective in Zone 1 for depths between 0 and 3 m below MLLW. 
Simulations for depths below 3 m (MLLW) exhibited trajectories which indicate that the 
25 percentile values were not protective. 


166 













200 


150 

100 

50 

0 

400 


0 m 
-0.5 m 



co 

0 

"co 

TD 

> 

SZ 

o 

-Q 

CO 

O 


E 

O 

O 

(f) 


300 


200 


S 100 


Year 1 


Year 2 


Year 3 


^ o c 

Year 4 


Figure E.6 Case 2, Zone 2; simulations using 25 th percentile light attenuation and DIN values. 
Stable Zostera marina biomass and carbohydrate trajectories indicate that the 25 th 
percentile values are protective in Zone 2 for depths between 0 and 1 m below MLLW. 
Simulations for depths below 1 m (MLLW) exhibited trajectories which indicate that the 
25 th percentile values were not protective. 




167 


















0 m 
-1 m 



Figure E.7 Case 3, Zone 1; simulations using 75 th percentile light attenuation and DIN values. 
Stable Zostera marina biomass and carbohydrate trajectories indicate that the 75 th 
percentile values are protective in Zone 1 for depths between 0 and 2 m below MLLW. 
Simulations for depths below 2 m (MLLW) exhibited downward trajectories which 
indicate that the 75 percentile values were not protective. 


168 














0 m 
-0.5 m 



fh # 

Figure E.8 Case 3, Zone 2; simulations using 75 percentile light attenuation and DIN values. 

Zoster a marina biomass and carbohydrate trajectories for depths greater than 0 m MLLW 

th 

exhibited downward trajectories which indicate that the 75 percentile values were not 
protective at these depths in Zone 2. 


169 


< t 


G-JO U.S. GOVERNMENT PRINTING OFFICE:2007 — 740-332 















































































































































































PRESORTED STANDARD 
POSTAGE & FEES PAID 
EPA 

PERMIT NO. G-35 


United States 
Environmental Protection 
Agency 


Office of Research and Development (81 OR) 
Washington, DC 20460 

Official Business 
Penalty for Private Use 
$300 



Recycled/Recyclable Printed on paper that contains a minimum of 
50% postconsumer fiber content processed chlorine free 





